What is capital market efficiency in finance

what is capital market efficiency in finance

includes monthly Morgan Stanley Capital International indices of 18 been frequently used in order to test the efficiency of financial markets. An efficient market is a place where the market prices of financial instruments like stocks reflect all information that is available. It also adjusts. earnings. Calculated as: Market value per share. Earnings per share. ❖ Dividend Yield. This is a financial ratio, which.

What is capital market efficiency in finance -

Corporations and Financial Markets , Macroeconomics, Taxes

The efficient markets theory (EMT) of financial economics states that the price of an asset reflects all relevant information that is available about the intrinsic value of the asset. Although the EMT applies to all types of financial securities, discussions of the theory usually focus on one kind of security, namely, shares of common stock in a company. A financial security represents a claim on future cash flows, and thus the intrinsic value is the present value of the cash flows the owner of the security expects to receive.

Theoretically, the profit opportunities represented by the existence of “undervalued” and “overvalued” stocks motivate investors to trade, and their trading moves the prices of stocks toward the present value of future cash flows. Thus, investment analysts’ search for mispriced stocks and their subsequent trading make the market efficient and cause prices to reflect intrinsic values. Because new information is randomly favorable or unfavorable relative to expectations, changes in stock prices in an efficient market should be random, resulting in the well-known “random walk” in stock prices. Thus, investors cannot earn abnormally high risk-adjusted returns in an efficient market where prices reflect intrinsic value.

As Eugene Fama (1991) notes, market efficiency is a continuum. The lower the transaction costs in a market, including the costs of obtaining information and trading, the more efficient the market. In the United States, reliable information about firms is relatively cheap to obtain (partly due to mandated disclosure and partly due to technology of information provision) and trading securities is cheap. For those reasons, U.S. security markets are thought to be relatively efficient.

The informational efficiency of stock prices matters in two main ways. First, investors care about whether various trading strategies can earn excess returns (i.e., “beat the market”). Second, if stock prices accurately reflect all information, new investment capital goes to its highest-valued use.

French mathematician Louis Bachelier performed the first rigorous analysis of stock market returns in his 1900 dissertation. This remarkable work documents statistical independence in stock returns—meaning that today’s return signals nothing about the sign or magnitude of tomorrow’s return—and this led him to model stock returns as a random walk, in anticipation of the EMT. Unfortunately, Bachelier’s work was largely ignored outside mathematics until the 1950s. One of the first to recognize the potential information content of stock prices was John Burr Williams (1938) in his work on intrinsic value, which argues that stock prices are based on economic fundamentals. The alternative view, which dominated prior to Williams, is probably best exemplified by John Maynard Keynes’s beauty contest analogy, in which each stock analyst recommends not the stock he thinks best, but rather the stock he thinks most other analysts think is best. In Keynes’s view, therefore, stock prices are based more on speculation than on economic fundamentals. In the long run, prices driven by speculation may converge to those that would exist based on economic fundamentals, but, as Keynes noted in another context, “in the long run we are all dead.”

Stock returns and their economic meaning received scant attention before the 1950s because there was little appreciation of the role of stock markets in allocating capital. This oversight had several contributing factors: (1) Keynes’s emphasis on the speculative nature of stock prices led many to believe that stock markets were little more than “casinos,” with no essential economic role; (2) many economists during the Great Depression and the immediate post–World War II era emphasized government-directed capital investment; and (3) the modern corporation, and the resulting need to raise large sums of capital, was a relatively recent development. But the invention of computing power in the 1950s, which made rigorous empirical analysis with large data sets more feasible, brought renewed attention from academic researchers.

In 1953, British statistician Maurice Kendall documented statistical independence in weekly returns from various British stock indices. Harry Roberts (1959) found similar results for the Dow Jones Industrial Index, and later, Eugene Fama (1965) provided comprehensive evidence not only of statistical independence in stock returns, but also that various techniques of “chartists” (i.e., technical analysts) had no predictive power. While this evidence was generally viewed as supporting the random walk model of stock returns, there was no formal understanding of its economic meaning, and some mistakenly took this randomness as an indication that stock returns were unrelated to fundamentals, and thus had no economic meaning or content. Fortunately, the timely work of Paul Samuelson (1965) and Benoit Mandelbrot (1966) explained that such randomness in returns should be expected from a well-functioning stock market. Their key insight was that competition implies that investing in stocks is a “fair game,” meaning that a trader cannot expect to beat the market without some informational advantage. The essence of the “fair game” is that today’s stock price reflects the expectations of investors given all the available information. Therefore, tomorrow’s price should change only if investors’ expectations of future events change, and such changes should be randomly positive or negative as long as investors’ expectations are unbiased. This revelation had its roots in the developing rational expectations theory of macroeconomics, and thus, some economists refer to the EMT as the “rational markets theory.” It was later recognized that the “fair game” model allows for the expectation of a positive price change, which is necessary to compensate risk-averse investors.

In 1970, Eugene Fama published his now-famous paper, “Efficient Capital Markets: A Review of Theory and Empirical Work.” Fama synthesized the existing work and contributed to the focus and direction of future research by defining three different forms of market efficiency: weak form, semistrong form, and strong form. In a weak-form efficient market, future returns cannot be predicted from past returns or any other market-based indicator, such as trading volume or the ratio of puts (options to sell stocks) to calls (options to buy stocks). In a semistrong efficient market, prices reflect all publicly available information about economic fundamentals, including the public market data (in weak form), as well as the content of financial reports, economic forecasts, company announcements, and so on. The distinction between the weak and semistrong forms is that it is virtually costless to observe public market data, whereas a high level of fundamental analysis is required if prices are to fully reflect all publicly available information, such as public accounting data, public information regarding competition, and industry-specific knowledge. In strong form, the highest level of market efficiency, prices reflect all public and private information. This extreme form serves mainly as a limiting case because it would require even the private information of corporate officers about their own firm to be already captured in stock prices.

A simple way to distinguish among the three forms of market efficiency is to recognize that weak form precludes only technical analysis from being profitable, while semi-strong form precludes the profitability of both technical and fundamental analysis, and strong form implies that even those with privileged information cannot expect to earn excess returns. Sanford Grossman and Joseph Stiglitz (1980) recognized that an extremely high level of market efficiency is internally inconsistent: it would preclude the profitable opportunities necessary to motivate the very security analysis required to produce information. Their main point is that market frictions, including the costs of security analysis and trading, limit market efficiency. Thus, we should expect to see the level of efficiency differ across markets, depending on the costs of analysis and trading. Although weak-form efficiency allows for profitable fundamental analysis, it is not difficult to imagine a market that is less than weak form but still relatively efficient in some sense. Thus, it can be useful to define the efficiency of a market in a more general, continuous sense, with faster price reaction equating to greater informational efficiency.

While most of the empirical research of the 1970s supported semistrong market efficiency, a number of apparent inconsistencies arose by the late 1970s and early 1980s. These so-called anomalies include, among others, the “small-firm effect” and the “January effect,” which together document the tendency of small-capitalization stocks to earn excessive returns, especially in January. But financial economists today attribute most of the anomalies to either misspecification of the asset-pricing model or market frictions. For example, the small-firm and January effects are now commonly perceived as premiums necessary to compensate investors in small stocks, which tend to be illiquid, especially at the turn of the year. Fama (1998) also notes that the anomalies sometimes involved underreaction and sometimes overreaction and, thus, could be viewed as random occurrences that often went away when different time periods or methodologies were used.

More serious challenges to the EMT emerged from research on long-term returns. Robert Shiller (1981) argued that stock index returns are overly volatile relative to aggregate dividends, and many took this as support for Keynes’s view that stock prices are driven more by speculators than by fundamentals. Related work by Werner DeBondt and Richard Thaler (1985) presented evidence of apparent overreaction in individual stocks over long horizons of three to five years. Specifically, the prices of stocks that had performed relatively well over three- to five-year horizons tended to revert to their means over the subsequent three to five years, resulting in negative excess returns; the prices of stocks that had performed relatively poorly tended to revert to their means, resulting in positive excess returns. This is called “reversion to the mean” or “mean reversion.” Lawrence Summers (1986) showed that, in theory, prices could take long, slow swings away from fundamentals that would be undetectable with short horizon returns. Additional empirical support for mispricing came from Narasimhan Jegadeesh and Sheridan Titman (1993), who found that stocks earning relatively high or low returns over three- to twelve-month intervals continued the trend over the subsequent three to twelve months.

These apparent inefficiencies contributed to the emergence of a new school of thought called behavioral finance (see behavioral economics), which countered the assumption of rational expectations with evidence from the field of psychology that people tend to make systematic cognitive errors when forming expectations. One such error that might explain overreaction in stock prices is the representative heuristic, which holds that individuals attempt to identify trends even where there are none and that this can lead to the mistaken belief that future patterns will resemble those of the recent past. On the other hand, momentum in stock returns may be explained by anchoring, the tendency to overweight initial beliefs and underweight the relevance of new information. It follows that momentum observed over intermediate horizons could be extrapolated over longer time horizons until overreaction develops. This does not, however, imply any easily exploitable trading strategy, because the point where momentum stops and overreaction starts will never be obvious until after the fact.

Resistance to the view that stock prices systematically overreact, as well as to the behavioral interpretation of this evidence, came along two fronts. First, Fama and Kenneth French (1988) found that stocks earn larger returns during more difficult economic conditions when capital is relatively scarce and the default-risk premiums in interest rates are high. Higher interest rates initially drive prices down, but eventually prices recover with improved business conditions, and hence the mean-reverting pattern in aggregate returns. Second, adherents of the EMT argued that the cognitive failures of certain individuals would have little influence on stock markets because mispriced stocks should attract rational investors who buy underpriced and sell overpriced stocks.

Critics of the EMT responded to both of these charges. In response to the Fama and French evidence, James Poterba and Lawrence Summers argued that the mean-reverting pattern in aggregate index returns is too volatile to be explained by cyclical economic conditions alone. They claimed that excessive mean reversion resulted from prices straying from fundamentals, similar to Shiller’s excess volatility story. As to whether the marginal trader is fully rational or subject to systematic cognitive errors, Andrei Shleifer and Robert Vishny (1997) and others noted that, while market efficiency requires traders to act quickly on their information out of fear of losing their advantage, mispricing can persist because it offers few opportunities for low-risk arbitrage trading. For example, how should one have responded during the bubble in Internet-based stocks of the late 1990s? Most of these stocks were difficult to short sell, and even if it was possible, a well-informed, fully rational short seller faced the risk that less than fully rational traders (also known as “noise traders”) would continue to move prices away from fundamentals. Thus, the market will not necessarily correct as soon as rational traders recognize mispricing. Instead, the correction may come only after the mispricing becomes so large that noise traders lose confidence in the trend or rational traders act in response to the additional risk introduced by the noise traders.

The most striking examples of apparent inconsistencies with the EMT are the 1987 stock market crash and the movement of Internet stock prices beginning in the late 1990s. Some economists, admittedly a minority, believe that the 1987 crash and the Internet run-up and fall are consistent with market efficiency. For example, Mark Mitchell and Jeffry Netter (1989) argued that the large market decline in the days before the market crash in 1987 was triggered by an initially rational response to an unanticipated tax proposal, which in turn triggered a temporary liquidity crunch (or panic) due to much higher sales volumes than the market was prepared to handle. The exchanges, traders, and regulators learned from this experience making markets more efficient. Burton Malkiel (2003a, 2003b), analyzing the Internet bubble, notes that Internet company values were difficult to determine, and while traders in most cases were wrong after the fact, there were no obvious unexploited arbitrage opportunities.

Regardless of whether it is the exception or the rule, the favorable market conditions of the late 1990s for technology and Internet-based stocks illustrate the stock market’s critical role in resource allocation. A firm whose stock has appreciated rapidly finds it easier to raise additional funds through a secondary offering because higher prices mean a smaller percentage ownership of the firm needs to be offered to raise a given amount of capital. Favorable conditions also make it easier for privately held firms to raise funds through an initial public offering (IPO) of stock. Furthermore, a so-called hot IPO market entices venture capital firms to invest funds in hot industries and sectors in hopes of taking their firms public in such a favorable market. Many view these favorable market conditions as consistent with the market’s valuation of growth options and the motivating incentive necessary to make the fundraising portion of venture growth and creation possible. But while favorable market conditions can attract the investment capital necessary to grow a fledgling new industry, the market for technology and Internet-based stocks in the late 1990s appears to have overheated and, in hindsight, directed too much investment capital toward this sector. Thus, by the late 1990s, the return an investor in this sector could have rationally expected had fallen below what economic conditions could justify, as well as below what most investors actually anticipated.

While prices may take long, slow swings away from fundamentals, the EMT is still useful in at least two important ways. First, over shorter horizons, such as days, weeks, or months, there is considerable evidence that the EMT can explain the direction of stock price changes. That is, the response of stock prices to new information reasonably approximates the change in the intrinsic value of equity. Second, the EMT serves as a benchmark for how prices should behave if capital investments and other resources are to be allocated efficiently. Just how close markets come to this benchmark depends on the transparency of information, the effectiveness of regulation, and the likelihood that rational arbitragers will drive out noise traders. In fact, the informational efficiency of stock prices varies across markets and from country to country. Whatever the shortcomings of capital markets, there appears to be no better alternative means of allocating investment capital. In fact, the privatization movement of the 1990s and early 2000s suggests that most governments, including China’s, now recognize this fact. Thus, academic inquiry in this area is likely to focus more on the conditions that explain and improve the informational efficiency of capital markets than on whether capital markets are efficient.

About the Authors

Steven L. Jones is an associate professor of finance at Indiana University’s Kelley School of Business, Indianapolis. Jeffry M. Netter is the C. Herman and Mary Virginia Terry Chair of Business Administration in the University of Georgia’s Terry College of Business. From 1986 to 1988, he was a senior research scholar at the U.S. Securities and Exchange Commission.

Further Reading


DeBondt, Werner F. M., and Richard Thaler. “Does the Stock Market Overreact?” Journal of Finance 40 (1985): 793–805.

Fama, Eugene F. “The Behavior of Stock Market Prices.” Journal of Business 38 (January 1965): 34–105.

Fama, Eugene F. “Efficient Capital Markets: A Review of Empirical Work.” Journal of Finance 25, no. 2 (1970): 383–417.

Fama, Eugene F. “Efficient Capital Markets II.” Journal of Finance 46, no. 5 (1991): 1575–1617.

Fama, Eugene F. “Market Efficiency, Long-Term Returns, and Behavioral Finance.” Journal of Financial Economics 49, no. 3 (1998): 283–306.

Fama, Eugene F., and Kenneth R. French. “Dividend Yields and Expected Stock Returns.” Journal of Financial Economics 22 (October 1988): 3–25.

Grossman, Sanford J., and Joseph E. Stiglitz. “On the Impossibility of Informationally Efficient Markets.” American Economic Review 70 (June 1980): 393–408.

Jegadeesh, Narasimhan, and Sheridan Titman. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance 48 (March 1993): 65–91.

Kendall, Maurice. “The Analysis of Economic Time Series, Part I: Prices.” Journal of the Royal Statistical Society 96 (1953): 11–25.

Keynes, John M. The General Theory of Employment, Interest and Money. New York: Harcourt, 1936.

Malkiel, Burton G. “The Efficient Market Hypothesis and Its Critics.” Journal of Economic Perspectives 17, no. 1 (2003a): 59–82.

Malkiel, Burton G. A Random Walk down Wall Street. 8th ed. New York: Norton, 2003b.

Mandelbrot, Benoit. “Forecasts of Future Prices, Unbiased Markets and ‘Martingale Models.’” Journal of Business, special supplement (January 1966): 242–255.

Mitchell, Mark, and Jeffry Netter. “Triggering the 1987 Stock Market Crash: Antitakeover Provisions in the Proposed House Ways and Means Tax Bill?” Journal of Financial Economics 24 (1989): 37–68.

Poterba, James M., and Lawrence Summers. “Mean Reversion in Stock Market Prices: Evidence and Implications.” Journal of Financial Economics 22 (1987): 27–59.

Roberts, Harry. “Stock Market ‘Patterns’ and Financial Analysis: Methodological Suggestions.” Journal of Finance 14 (1959): 11–25.

Samuelson, Paul. “Proof that Properly Anticipated Prices Fluctuate Randomly.” Industrial Management Review 6 (1965): 49.

Shiller, Robert J. “Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?” American Economic Review 71 (June 1981): 421–435.

Shiller, Robert J. “From Efficient Markets to Behavioral Finance.” Journal of Economic Perspectives 17, no. 1 (2003): 83–104.

Shleifer, Andrei, and Robert W. Vishny. “The Limits of Arbitrage.” Journal of Finance 52 (March 1997): 35–55.

Summers, Lawrence. “Does the Stock Market Rationally Reflect Fundamental Values?” Journal of Finance 41 (July 1986): 591–601.

Williams, John Burr. The Theory of Investment Value. Cambridge: Harvard University Press, 1938.



For an excellent review of the debate on market efficiency, see Shiller 2003 for the behavioral finance view, and Malkiel 2003a for the proefficiency view.



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Efficient Capital Markets and Corporate Finance

P.V. Viswanath


Firms can create value from financing decisions in the following ways:

  1. Fool investors.
  2. Reduce costs or increase subsidies; i.e. adopt NPV > 0 projects and choose an optimal capital structure and dividend policy.
  3. create a new security that satisfies a market need.

Financial Innovation is a difficult and high-risk strategy for value creation; which leaves us the first two strategies.

Empirical evidence suggests that the capital markets are informationally efficient, which rules out alternative one, leaving only alternative two as a potential source of value.

What is market efficiency?

An efficient market is a market in which securities are priced according to their value given all publicly available information.

Consequently, a manager cannot usually hope to manipulate the market value of a firm. However, the fact that all relevant information is not available publicly means that the manager may be able to use his private information to choose financing strategies that do indeed dispossess the outside investor (e.g. selling overvalued stock). However, this generates agency costs, and hence does not work as a long term strategy.

With the above qualification, capital market efficiency implies:

  • Financial Managers cannot time issues of bonds and stocks.
  • A firm can sell as many shares of stocks or bonds as it wants without fear of depressing price (except in the very short run).
  • Stock and bond markets cannot be affected by firms artificially increasing earnings (that is, cooking the books).
  • An announcement of an accounting method change should not affect stock prices if enough information was available previously to enable individual investors to independently construct the accounting numbers generated under the new method. Stock prices may change when an accounting change is announced, if new information is revealed.

Information and Market Price Changes

Apparent evidence of market inefficiency may only indicate that new information is being revealed.

Example 1: Consider the LIFO vs FIFO decision. Several studies found that the market price of the stock rose on the switch to LIFO, and that the stock price increase was correlated with the size of the tax benefits. Why should there have been any effect at all? Did the market not know the tax effect of the switch before the announcement? Why was the switch not anticipated?

Example 2: In another study, Fama, Fisher, Jensen and Roll investigated the market reaction to 940 stock splits. They found that the market price rose for stock splits overall. But stock splits have no effect on cash flows! Is this a market inefficiency?

Possible explanations:


In inflationary times, the LIFO method allows the most expensive inventory to be used as the source of goods sold. This reduces taxable earnings; hence LIFO is preferable in such times.On the other hand, FIFO improves reported profits in times of inflation.

The eagerness with which a firm converts to FIFO could tell us the rate of turnover of its inventory, and its age. Unwillingness to convert could signal an undue preoccupation with reported income, perhaps because executive compensation is based on reported income.

Stock Splits:

FFJR found in their sample that stock splits that induced stock price rises tended to be followed by dividend increases. In other words, the stock split was an indicator of a dividend increase, which would indeed be new information.

(Question for the reader: Why should there be any correlation between stock splits and dividend increases?)

Overreaction and Delayed Reaction

There seems to be some evidence that investors 'overreact.'

(Question for the reader: Is it possible for the 'overreaction' (and perhaps even the 'delayed reaction') sequence to be efficient? )

Implications of Market Efficiency for Capital Budgeting

A project can be reliably identified as being positive NPV only if we can also identify the sources of that positive NPV. In general, the sources of such value enhancement represent some deviation from perfect competition in the product market, such as the existence of barriers to entry in the firm's industry, due to:

  1. the availability of economies of scale in production
    Lesson: Investments that are structured to exploit economies of scale are more likely to be successful than those that are not.
  2. the possibility of product differentiation
    Lesson: Investments designed to create a position at the high end of anything, including the high end of the low end, differentiated by a quality or service edge, will generally be profitable.
  3. cost advantages
    Lesson: Investments aimed at achieving the lowest delivered cost position in the industry, coupled with a pricing policy to expand market share, are likely to succeed, especially if the cost reductions are proprietary.
  4. monopolistic access to distribution channels
    Lesson: Investments devoted to gaining better product distribution often lead to higher profitability.
  5. protective government regulation
    Lesson: Investments in project protected from competition by government regulation can lead to extraordinary profitability. However, what the government gives, the government can take away!

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What is the Efficient Market Hypothesis?

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The efficient market hypothesis states that share prices reflect all relevant information, and that it is impossible to beat the market or achieve above-average returns on a sustainable basis. There are many critics of this theory, such as behavioral economists, who believe in inherent market inefficiencies.

The background and idea behind the efficient market hypothesis

The efficient market hypothesis was developed from a Ph.D. dissertation by economist Eugene Fama in the 1960s, and essentially says that at any given time, stock prices reflect all available information and trade at exactly their fair value at all times. Therefore, it is impossible to consistently choose stocks that will beat the returns of the overall stock market. Basically, the hypothesis implies that the pursuit of market-beating performance is more about chance than it is about researching and selecting the right stocks.

Three variations

There are three levels, or degrees, of the efficient market hypothesis: weak, semi-strong, and strong.

The weak form assumes that current stock prices reflect all available information, and that past price performance has no relationship with the future. In other words, this form of the hypothesis says that using technical analysis to achieve exceptional returns is impossible.

The semi-strong form says that stock prices have factored in all available public information. Because of this, it's impossible to use fundamental analysis to choose stocks that will beat the market's returns.

Finally, the strong form of the efficient market hypothesis says that all information -- public as well as private -- is incorporated into current stock prices. This form of the efficient market hypothesis essentially assumes a perfect market, and isn't plausible when there are insider trading restrictions.

Criticisms of the hypothesis

Perhaps the biggest piece of evidence to refute the efficient market hypothesis is the existence of market bubbles and crashes. For example, if the assumptions of the hypothesis were correct, the housing bubble and stock market crash of 2008 wouldn't have happened. The same can be said about the tech bubble of the late 1990s, when many tech companies were trading for sky-high valuations before crashing.

Also, there are some investors who have consistently beaten the market. As a famous example, Warren Buffett has been highly critical of the efficient market hypothesis. Using his value investing approach and trying to identify a margin of safety in stocks, Buffett has achieved returns that have been far superior to those of the market -- and he's done it steadily over a 50-year period of time.

Behavioral economists are also major critics of the efficient market hypothesis. In a nutshell, the study of behavioral finance is based on the assumption that investors are susceptible to certain biases, such as the belief that past performance is indicative of the future. These biases can lead to mispricings in stocks, according to proponents.

This article is part of The Motley Fool's Knowledge Center, which was created based on the collected wisdom of a fantastic community of investors. We'd love to hear your questions, thoughts, and opinions on the Knowledge Center in general or this page in particular. Your input will help us help the world invest, better! Email us at [email protected] Thanks -- and Fool on!

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I am happy to address this conference, now in its eighth year, and endorse Atlanta Fed President Jack Guynn's choice of topic. Many of us, with the benefit of hindsight, have been endeavoring for nearly two years to distill the critical lessons from the global crises of 1997 and 1998. Your contributions to this analysis are timely and useful.

Knowing that you have touched on a number of topics over the last few days, I wanted to focus on some of the issues I raised at the most recent meetings of the IMF and World Bank; in particular, why the financial turmoil engendered by disruption in Asia resulted in a crisis longer and deeper than we expected in its early days. In a sense, I am turning the question posed by this conference--Do efficient financial markets contribute to financial crises?--on its head by asking whether efficient financial markets mitigate financial crises.

To answer the question, we need to look at the financial market situation of just over a year ago. Following the Russian default of August 1998, public capital markets in the United States virtually seized up. For a time, not even investment-grade bond issuers could find reasonable takers. While Federal Reserve easing shortly thereafter doubtless was a factor, it is not credible that this move fully explained the dramatic restoration of most, though not all, markets in a matter of weeks. The problems in our markets appeared too deep-seated to be readily unwound solely by a cumulative 75 basis point ease in overnight rates.

Arguably, at least as important was the existence of backup financial institutions, especially commercial banks, that replaced the intermediation function of the public capital markets. As public debt issuance fell, commercial bank lending accelerated, effectively filling in some of the funding gap. Even though bankers also moved significantly to risk aversion, previously committed lines of credit, in conjunction with Federal Reserve ease, were an adequate backstop to business financing, and the impact on the real economy of the capital market turmoil was blunted. Firms were able to sustain production, and business and consumer confidence was not threatened. A vicious circle of the initial disruption leading to losses and then further erosion in the financial sector never got established.

Capital Market Alternatives

What we perceived in the United States in 1998 may reflect an important general principle: Multiple alternatives to transform an economy's savings into capital investment act as backup facilities should the primary form of intermediation fail. In 1998 in the United States, banking replaced the capital markets. Far more often it has been the other way around, as it was most recently in the United States a decade ago.

When American banks stopped lending in 1990, as a consequence of a collapse in the value of real estate collateral, the capital markets were able to substitute for the loss of bank financial intermediation. Interestingly, the then recently developed mortgage-backed securities market kept residential mortgage credit flowing, which in prior years would have contracted sharply. Arguably, without the capital market backing, the mild recession of 1991 could have been far more severe.

Our mild recession in 1991 offers a stark contrast with the long-lasting problems of Japan, whose financial system is an example of predominantly bank-based financial intermediation. The keiretsu conglomerate system, as you know, centers on a "main bank," leaving corporations especially dependent on banks for credit. Thus, one consequence of Japan's banking crisis has been a protracted credit crunch. Some Japanese corporations did go to the markets to pick up the slack. Domestic corporate bonds outstanding have more than doubled over the decade while total bank loans have been almost flat. Nonetheless, banks are such a dominant source of funding in Japan that this increase in nonbank lending has not been sufficient to avert a credit crunch.

The Japanese government is injecting funds into the banking system in order to recapitalize it. While it has made some important efforts, it has yet to make significant progress in diversifying the financial system. This could be a key element, although not the only one, in promoting long-term recovery. Japan's banking crisis is also ultimately likely to be much more expensive to resolve than the American crisis, again providing prima facie evidence that financial diversity helps limit the effect of economic shocks.

This leads one to wonder how severe East Asia's problems would have been during the past eighteen months had those economies not relied so heavily on banks as their means of financial intermediation. One can readily understand that the purchase of unhedged short-term dollar liabilities to be invested in Thai baht domestic loans would at some point trigger a halt in lending by Thailand's banks if the dollar exchange rate did not hold. But why did the economy need to collapse when lending did? Had a functioning capital market existed, along with all the necessary financial infrastructure, the outcome might well have been far more benign.

Before the crisis broke, there was little reason to question the three decades of phenomenally solid East Asian economic growth, largely financed through the banking system. The rapidly expanding economies and bank credit growth kept the ratio of nonperforming loans to total bank assets low. The failure to have backup forms of intermediation was of little consequence. The lack of a spare tire is of no concern if you do not get a flat. East Asia had no spare tires.

Managing Bank Crises

Banks, being highly leveraged institutions, have, throughout their history, periodically fallen into crisis. The classic problem of bank risk management is to achieve an always-elusive degree of leverage that creates an adequate return on equity without threatening default.

The success rate has never approached 100 percent, except where banks are credibly guaranteed, usually by their governments, in the currency of their liabilities. But even that exception is by no means ironclad, especially when that currency is foreign. One can wonder whether in the United States of the nineteenth century, when banks were also virtually the sole intermediaries, numerous banking crises would have been as disabling if alternative means of intermediation were available.

In dire circumstances, modern central banks have provided liquidity, but fear is not always assuaged by cash. Even with increased liquidity, banks do not lend in unstable periods. The Japanese banking system today is an example: The Bank of Japan has created massive liquidity, yet bank lending has responded little. But unlike the United States a decade ago, alternative sources of finance are not yet readily available.

The case of Sweden's banking crisis in the early 1990s, in contrast to America's savings and loan crisis of the 1980s and Japan's current banking crisis, illustrates another factor that often comes into play with banking sector problems: Speedy resolution is good, whereas delay can significantly increase the fiscal and economic costs of a crisis. Resolving a banking-sector crisis often involves government outlays because of implicit or explicit government safety net guarantees for banks. Accordingly, the political difficulty in raising taxpayer funds has often meant delayed resolution. Delay, of course, can add to the fiscal costs and prolong a credit crunch.

Experience tells us that alternatives within an economy for the process of financial intermediation can protect that economy when one of those financial sectors undergoes a shock. Australia serves as an interesting test case in the most recent Asian financial turmoil. Despite its close trade and financial ties to Asia, the Australian economy exhibited few signs of contagion from contiguous economies, arguably because Australia already had well-developed capital markets as well as a sturdy banking system. But going further, it is plausible that the dividends of financial diversity extend to more normal times as well. The existence of alternatives may well insulate all aspects of a financial system from breakdown.

Diverse capital markets, aside from acting as backup to the credit process in times of stress, compete with a banking system to lower financing costs for all borrowers in more normal circumstances. Over the decades, capital markets and banking systems have interacted to create, develop, and promote new instruments that improved the efficiency of capital creation and risk bearing in our economies. Products for the most part have arisen within the banking system, where they evolved from being specialized instruments for one borrower to having more standardized characteristics.

At the point that standardization became sufficient, the product migrated to open capital markets, where trading expanded to a wider class of borrowers, tapping the savings of larger groups. Money market mutual funds, futures contracts, junk bonds, and asset-backed securities are all examples of this process at work.

Once capital markets and traded instruments came into existence, they offered banks new options for hedging their idiosyncratic risks and shifted their business from holding to originating loans. Bank trading, in turn, helped these markets to grow. The technology-driven innovations of recent years have facilitated the expansion of this process to a global scale. Positions taken by international investors within one country are now being hedged in the capital markets of another: so-called proxy hedging.

Building Financial Infrastructure

But developments of the past two years have provided abundant evidence that where a domestic financial system is not sufficiently robust, the consequences for a real economy of participating in this new, complex global system can be most unwelcome.

It is not surprising that banking systems emerge as the first financial intermediary in market economies as economic integration intensifies. Banks can marshal scarce information about the creditworthiness of borrowers to guide decisions about the allocation of capital. The addition of capital market alternatives is possible only if scarce real resources are devoted to building a financial infrastructure--a laborious process whose payoff is often experienced only decades later. The process is difficult to initiate, especially in emerging economies that are struggling to edge above the poverty level, because of the perceived need to concentrate on high short-term rates of return to capital rather than to accept more moderate returns stretched over a longer horizon.

We must continually remind ourselves that a financial infrastructure is composed of a broad set of institutions whose functioning, like all else in a society, must be consistent with the underlying value system. On the surface, financial infrastructure appears to be a strictly technical concern. It includes accounting standards that accurately portray the condition of the firm, legal systems that reliably provide for the protection of property and the enforcement of contracts, and bankruptcy provisions that lend assurance in advance as to how claims will be resolved in the inevitable result that some business decisions prove to be mistakes. Such an infrastructure promotes transparency within enterprises and allows corporate governance procedures that facilitate the trading of claims on businesses using standardized instruments rather than idiosyncratic bank loans. But the development of such institutions almost invariably is molded by the culture of a society. Arguably the notion of property rights in today's Russia is subliminally biased by a Soviet education that inculcated a highly negative view of individual property ownership. The antipathy to the "loss of face" in Asia makes it difficult to institute, for example, the bankruptcy procedures of Western nations, and in the West we each differ owing to deep-seated views of creditor-debtor relationships. Corporate governance that defines the distribution of power invariably reflects the most profoundly held societal views about the appropriate interaction of parties in business transactions. It is thus not a simple matter to append a capital markets infrastructure to an economy developed without it. Accordingly, instituting convergence across countries of domestic financial infrastructures or even of the components tied to international transactions is a very difficult task.

Indeed, weaknesses in financial infrastructure made Asian banking systems more vulnerable before the crisis and have impeded resolution of the crisis subsequently. Lack of transparency coupled with an implicit government guarantee for banks encouraged investors to lend too much to banks too cheaply, with the consequence that capital was not allocated efficiently. Poor bankruptcy laws and procedures have made recovery on nonperforming bank loans a long and costly procedure. Moreover, the lack of transparency and of a legal infrastructure for enforcing contracts and collecting debts in Russia are a prime cause of the dearth of financial intermediation in Russia at this time.

Nonetheless, the competitive pressures toward convergence will be a formidable force in the future if, as I suspect, additional forms of financial intermediation are seen as benefiting an economy. Moreover, a broader financial infrastructure will likely also strengthen the environment for the banking system and enhance its performance.

A recent study by Ross Levine and Sara Zervos suggests that financial market development improves economic performance, over and above the benefits offered by banking sector development alone. The results are consistent with the idea that financial markets and banks provide useful, but different, bundles of financial services and that utilizing both will almost surely result in a more robust and more efficient process of capital allocation.

It is no coincidence that the lack of adequate accounting practices, bankruptcy provisions, and corporate governance have been mentioned as elements in several of the recent crises that so disrupted some emerging-market countries. Had these been present, along with the capital markets they would have supported, the consequences of the initial shocks of early 1997 might well have been quite different.

It is noteworthy that the financial systems of most continental European countries escaped much of the turmoil of the past two years. And looking back over recent decades, we find fewer examples in continental Europe of banking crises sparked by real estate booms and busts or episodes of credit crunch of the sort I have mentioned in the United States and Japan.

Until recently, the financial sectors of continental Europe were dominated by universal banks, and capital markets are still less well developed there than in the United States or the United Kingdom. The experiences of these universal banking systems may suggest that it is possible for some bank-based systems, when adequately supervised and grounded in a strong legal and regulatory framework, to function robustly. But these banking systems have also had substantial participation of publicly owned banks. Such institutions rarely exhibit the dynamism and innovation that many private banks have employed for their, and their economies', prosperity. Government participation often distorts the allocation of capital to its most productive uses and undermines the reliability of price signals. But at times when market adjustment processes might have proved inadequate to prevent a banking crisis, such a government presence in the banking system can provide implicit guarantees of resources to keep credit flowing, even if its direction is suboptimal.

In Germany, for example, publicly controlled banking groups account for nearly 40 percent of the assets of all banks taken together. Elsewhere in Europe, the numbers are less but still sizable. In short, there is some evidence to suggest that insurance against destabilizing credit crises has been purchased with a less efficient utilization of capital. It is perhaps noteworthy that this realization has helped engender a downsizing of public ownership of commercial banks in Europe, coupled with rapid development of heretofore modest capital markets, changes which appear to be moving continental Europe's financial system closer to the structure evident in Britain and the United States.

Continental European countries may gain an additional benefit from the increased development of their capital markets. With increased concentration of national banking systems, which will likely be followed by increased concentration of Europe-wide banking, comes the risk of an unusually large impact should the health of a megabank become impaired, causing the bank to curtail its lending. Having well-developed capital markets would likely help to mitigate these effects, as more firms would have alternative sources of funds.


Improving domestic banking systems in emerging markets will help to limit the toll of the next financial disturbance. But if, as I presume, diversity within the financial sector provides insurance against a financial problem turning into economy-wide distress, then steps to foster the development of capital markets in those economies should also have an especial urgency. Moreover, the difficult groundwork for building the necessary financial infrastructure--improved accounting standards, bankruptcy procedures, legal frameworks, and disclosure--will pay dividends of their own.

The rapidly developing international financial system has clearly intensified competitive forces that have enhanced standards of living throughout most of the world. It is important that we develop domestic financial structures that facilitate and protect our international financial and trading systems, a process that will require much energy and commitment in the years ahead.

Источник: https://www.federalreserve.gov/boarddocs/speeches/1999/19991019.htm

Does Automation Improve Stock Market Efficiency? Evidence from Ghana


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Источник: https://mpra.ub.uni-muenchen.de/43642/

Definition of 'Capital Market'

Definition: Capital market is a market where buyers and sellers engage in trade of financial securities like bonds, stocks, etc. The buying/selling is undertaken by participants such as individuals and institutions.

Description: Capital markets help channelise surplus funds from savers to institutions which then invest them into productive use. Generally, this market trades mostly in long-term securities.

Capital market consists of primary markets and secondary markets. Primary markets deal with trade of new issues of stocks and other securities, whereas secondary market deals with the exchange of existing or previously-issued securities. Another important division in the capital market is made on the basis of the nature of security traded, i.e. stock market and bond market.

Also See: Capital, Debt, Portfolio Weight, Secondary Market, Allocational Efficiency

Источник: https://economictimes.indiatimes.com/definition/capital-market

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This study examines the weak-form efficient market hypothesis (EMH) for the Finance Sector in Malaysian Stock Exchange, by exploring and scrutinizing the firm-level efficiency over for the period from 1st January 1997 to 31st December 2014. For this purpose, we apply panel nonlinear unit root test that accounts for heterogeneity, and panel stationarity test to allow for the presence of structural breaks and cross-sectional dependence (CSD). The main findings of this study suggest the following: first, there is a strong CSD among the price series of finance stocks; second, unlike the traditional panel unit root tests that provide mixed-results, the panel stationarity test which incorporates structural breaks and CSD suggests that these series are characterized as random walk processes implying the Finance Sector is weak-form efficient. The finding of weak-form efficiency has salient implications in terms of capital allocation, stock price predictability, forecasting technique, and the impact of shocks to stock prices.


Market efficiency

Financial firms



Panel data

Structural breaks

Cross-sectional dependence

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Eficiencia del mercado

Entidades financieras



Datos del panel

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Investors generally believe that stock prices are predictable based on the information of past trading, which this motivates short horizon trading and speculation in the stock market. Technical analysis hints that stock price series will enrol in trends which tend to repeat in future (Dana & Cristina, 2013). The basic tenet of technical theories stipulates that historical stock price behaviour tends to recur in future, thus successive price changes are dependent and can predict future price movements (Fama, 1965). For example, the Dow Theory states that markets are likely to move in trends until reversals are exhibited, in which a primary trend can be either bear or bull market and a secondary trend displays short-term reversal(s) in the primary trend (Ray, 2012). As investors expect to earn excess profits by exploiting any observable trends in stock price series, they prefer to trade for short-term (days, weeks, or months) as well as to speculate rather than using a strategy of simply buy-and-hold.

Short-term investment and speculation in the stock market may cause tremendous adverse impacts on an economy. First, the smooth functioning of stock market in facilitating long-term investment can be disrupted. Levine (1996) has mentioned that, some analysts view the stock markets in developing countries as casinos which have little impact on economic growth. Second, short horizon trading may cause the amplification of market shocks. Cella (2013) found that during market turmoil either as a consequence of actual shocks or due to the fear of future shocks, short-horizon institutional investors have tendency to sell their stockholdings to a large extent, causing the amplification of market shocks. Third, a market which is largely occupied by investors with short investment horizon is likely to be exposed to higher risk of capital inflow reversal. Radelet and Sachs (1998) highlight that, some Asian countries including Malaysia suffered from foreign capital sudden withdrawals during the outbreak of Asian financial crisis in 1997-1998.

However, the prediction of stock prices based on past price information is refuted under the weak-form EMH. The hypothesis claims that stock prices already fully reflect the information contained in the history of past trading (Bodie, Kane, & Marcus, 2008). Consistent with the hypothesis, the theory of random walks affirms that stock price changes have no memory thus historical price data cannot predict the future price movements in any meaningful way (Fama, 1965). The random walk hypothesis (RWH) states that successive price changes are independent, and a random walk model is used to describe the stochastic system of security return generating process. The model is usually applied to examine the weak-form EMH.

To date, the weak-form EMH has remained vital to dictate the behaviour of stock prices. It has several salient implications. First, efficiency is essential in policy making as it enables an effective allocation of capital across different productive sectors in an economy. If market is efficient, stock prices give accurate signals to guide investor decision making. In that sense, efficiency helps to enhance stock market liquidity and in turn stimulates long-term investment and economic growth. Conversely, inefficiency causes higher information costs and fluctuations in stock prices (Hubbard, 2008). Second, if efficiency holds, the information of past trading is useless for predicting future stock price movements. Third, efficiency implies that stock prices fully reflect the information of past trading, thus technical analysis is not likely to be effective in stock price forecast. Fourth, if a stock price series can be characterized as a random walk process, shocks to stock prices are permanent. Following a shock, stock prices will reach a new equilibrium. Hence, future price movements cannot be predicted based on past price information (Narayan & Smyth, 2004; Munir, Kok, & Furouka, 2012). However, if a stock price series displays mean reversion, the impact of shocks to prices is transitory.

Distinct from the majority of past studies emphasizing on the aggregate stock markets, this present study aims to examine the weak-form EMH for the Finance Sector in Malaysian Stock Exchange by exploring and scrutinizing the efficiency of individual finance stocks. This study covers the period from 1st January 1997 until 31st December 2014. We notice that, there are a number of studies on the weak-form efficiency of Malaysian stock market. Some studies report that the market is efficient, for examples, Barnes (1986), Laurence (1986), Annuar and Shamsher (1993a; 1993b; 1994), Kok and Lee (1994), Kok and Goh (1995), Lim, Liew, and Wong (2005), and Munir and Mansur (2009). However, Lai, Balachandher, and Fauzias (2003) found that the Malaysian stock market is inefficient. Further, the findings of Lim (2008) and Cheong (2008) show the evidence of sectoral inefficiency in Malaysia. So far, very limited studies have examined the weak-form EMH using the sample of individual finance stocks. For instance, Lim, Tan, and Law (2007) examine the random walk property of the return series of four bank stocks namely, Hong Leong Bank, Malayan Banking, Public Bank, and Southern Bank, by employing the data until June 2004. Firm-level disaggregation is necessary to pinpoint the efficiency for individual finance stocks. The findings of Narayan, Narayan, Popp, and Ahmad (2015) show that only 15 percent from the total 34 banking firms listed on the New York Stock Exchange are weak-form efficient. This reflects financial firms are heterogeneous.

Finance stock weak-form efficiency in Malaysia is the focus in this study. The motivations for considering the finance stocks are as follows: Firstly, the efficiency of finance stocks in Malaysia has direct implication on the effectiveness of capital allocation across various domestic financial firms (i.e. financial holding companies, commercial banks, insurance companies, capital market intermediaries, and finance companies). This is concerned with economic growth, the core of the country's macroeconomic goal. There are two recent policies highlighting the importance of the domestic finance sector in fostering economic growth. Over the last ten-year period, the Financial Sector Masterplan 2001-2010 (FSMP) has successfully enhanced the capacity of the domestic finance sector and promoted greater stability in the financial system. Throughout this period, policy makers emphasized more on building institutional capacity and strengthening the financial system. After FSMP ends, the Financial Sector Blueprint 2011-2020 was introduced to transform Malaysia into a developed and high income country by the year 2020. In order to reach this target, the size of the financial system has to enlarge by eight to eleven percent per year (Central Bank of Malaysia, 2011). At this stage, the growth-stimulating role of the domestic finance sector is deemed important. As clearly outlined in the Financial Sector Blueprint 2011-2020, the domestic finance sector plays three key roles: the role as an enabler to facilitate the transition of Malaysia to become a high value-added and high-income country by the year 2020; the role as a driver to generate higher national income; and the role as a catalyst to foster a rapid growth in the new niche industries.

Secondly, the importance of finance stock efficiency is due to the critical concern that finance stocks are highly vulnerable as compared to other stocks. During the time of market turmoil, fluctuations in stock market may not show the true picture of vulnerable sectors. However, finance stocks are fragile particularly during financial and banking crises, and the impact of crisis shocks to stock prices can be widespread via potential contagion effect. For listed banks, default risk is systemic or non-diversifiable (Fiordelisi & Marqués-Ibañez, 2013). In addition, banks are more fragile than other firms because of inherent maturity mismatch on their balance sheets which exposes them to potential bank runs.1 Moreover, banking sector contagion is more strong and rapid than other sectors. Further, banks are exposed to shocks emanating from financial system because banks interact most with the real economy (Narayan et al., 2015). Similarly, other financial firms are also exposed to shocks arising from financial system due to their participation in the provision of credit and financial investment products. Finance stock vulnerable and the likelihood of contagion effect are the important concerns of policy makers. To certain extent, these may indicate that financial markets do not respond to shocks from financial system in an efficient manner. The puzzling aspect is not why value has tumbled, but why it ever got so high in the first place (Howell & Bain, 2005). Finance stock efficiency is concerned with the resilience of finance sector. In addition, the prices of finance stocks in an efficient market may signal the emergence of financial crisis. As mentioned by Miller and Luangaram (1998), ‘Asset prices can play a key role in signalling concern ex ante and in exacerbating problems when the crisis occurs.’

Nevertheless, bank stock efficiency is relatively more important than the efficiency of other finance stocks. Banks act as the traditional financial intermediaries in many countries. Bank credit predicts the growth in output, capital stock, and productivity (Levine & Zervos, 1998). Moreover, banks are still the key elements in financial system as they owned most of the non-bank financial intermediaries (FitzGerald, 2006).

In this contribution, the main methods used for analysis are the panel nonlinear heterogeneous unit root test developed by Ucar and Omay (2009), and the panel stationarity test advanced by Carrión-i-Silvestre, Barrio-Castro, and López-Bazo (2005) that accommodates the presence of multiple structural breaks and exploits the cross-section variations. In addition, we apply different univariate and panel unit root tests. The motivations for our choice of methodology are clear. Panel data analysis has an advantage of allowing for heterogeneity in individuals, firms, regions and countries, which is absent when using aggregated time series data (Baltagi, 2001). Further, we notice that structural breaks and CSD have not received much attention in the past panel based studies on the stock market efficiency in Malaysia. Unit root tests that do not account for structural breaks will have low power (Perron, 1989). Meanwhile, failing to take into account CSD may cause biased estimates and spurious inference (Chudik, Pesaran, & Tosetti, 2009). Thus, we simultaneously consider structural breaks and CSD in this present study.

Figure 1 depicts the log daily price series of selected Malaysian finance stocks which exhibit major dips around the time of the Asian financial crisis in 1997-1998. There may be other structural breaks in the series. Further, we believe that CSD may exist among the selected financial firms. These firms are highly interdependent in the financial system. International Monetary Fund (IMF, 2014) reveals that in Malaysia, banks, non-bank financial firms, and mutual funds have been highly interconnected through the domestic wholesale funding market.

The rest of this paper is structured as follows: Section 2 surveys the empirical literature. Section 3 discusses the datasets and methodology used. Section 4 reports and illuminates the empirical results. Last section summarizes and concludes the whole paper.

2Literature review

Until currently, finance stock weak-form efficiency has received very little attention from researchers. Most past studies concentrate on the semi-strong form efficiency of particular bank stocks, but less considering the weak-form efficiency and other finance stocks.

There are a number of past studies on the relationship between firm's operational or technical efficiency and stock returns. Kirkwood and Nahm (2005) study a sample of ten retail banks listed on Australian Stock Exchange for the period of 1995-2002. The data envelopment analysis (DEA) approach is utilized to construct the efficiency frontier. The sources of stock returns are analyzed based on a model of excess return, where profit efficiency is an explanatory variable and captures both revenue and cost efficiencies. Bank's operation efficiency is found to have significant prediction ability on stock returns, implying the semi-strong form EMH is rejected.

Ioannidis, Molyneux, and Pasiouras (2008) study the returns of bank stocks and publicly available information over the period of 2000-2006. The cost and profit efficiencies are estimated for 19 Asian and Latin American public listed banks, including 260 commercial banks and bank holding companies. The finding of study shows that there exists a robust relationship between the changes in profit efficiency with the returns of bank stocks, suggesting profit efficiency can explain bank stock returns better than the traditional profits measure of return on equity.

Kasman and Kasman (2011) investigate the link between the performance of Turkish commercial bank stocks proxy by cumulative annual stock returns computed on monthly basis, with three measures of bank performance including technical efficiency, scale efficiency, and productivity. Efficiency is measured based on the DEA approach, then stock returns are regressed against the changes in bank efficiency measures and bank specific control variables. Over the period of 1998-2008, stock performance is significantly and positively affected by the changes in all three bank efficiency measures.

Janoudi (2014) investigates the relationship between bank efficiency and stock performance in the EU markets using 141 commercial banks over the period 2004-2010. In terms of bank efficiency, the study focuses on both cost and profit efficiencies. Using the stochastic frontier analysis (SFA), the cost and profit efficiencies of the EU banking sectors are estimated. The study further investigates if the changes in cost and profit efficiencies are reflected in the annual stock returns of banks. The finding of study indicates that the effects from both cost and profit efficiencies are significant. It suggests that stocks with cost and profit efficiencies tend to outperform their inefficient counterparts.

Different from the above previous studies, Gaganis, Hasan, and Pasiouras (2013) shift their attention towards insurance industry to further explore the relation between stock returns and firm efficiency. The sample employed consists of 399 insurance companies traded in the stock markets of 52 countries, and the period of study spans from 2002 to 2008. This study found significant positive relationship between profit efficiency and stock returns. However, there is no robust indication for the nexus between cost efficiency and stock returns.

In Malaysia, Habibullah, Makmur, Wan Ngah, Alias, and Ong (2005) find that bank stocks are inefficient in the semi-strong form because the information of bank technical efficiency has significant forecast power on bank stock returns. The DEA method is used to compute the overall technical efficiency for banks, in which the efficiency is decomposed into pure technical, scale and congestion efficiencies. Then, the relationship between technical efficiency and bank stock returns is analyzed. It is found that, the percentage of change in stock prices reflects the percentage of change in the overall technical efficiency.

Sufian and Majid (2007) investigate the X-efficiency and P-efficiency of the Malaysian banks listed on the Kuala Lumpur Stock Exchange during the period of 2002-2003. Using the DEA method, the results indicate that the X-efficiency of banks is on average significantly higher than the P-efficiency. The larger banks are associated with relatively higher X-efficiency, and the smaller banks show higher P-efficiency. Bank stock prices are seen reacting more to the improvement in P-efficiency as compared to X-efficiency.

Sufian and Haron (2009) examine the efficiency of Malaysian banking sector by using a sample of seven banks listed on Kuala Lumpur Stock Exchange (KLSE), including Affin Bank Berhad, Bumiputera Commerce Bank Berhad, EON Bank Berhad, Maybank Berhad, Public Bank Berhad, Rashid Hussain Bank Berhad, and Southern Bank Berhad. Efficiency is estimated by employing individual bank market data and the DEA method. The findings reveal that Southern Bank Berhad is the most efficient bank, in which it is highly ranked in terms of returns with relatively low standard deviation and beta. All other banks which appear on the efficiency frontier display relative higher mean returns and lower standard deviations and betas. Since the returns on bank stocks are predictable based on the information of bank efficiency, thus the semi-strong form EMH is rejected.

However, the literature of finance stock weak-form efficiency is very limited. Stengos and Panas (1992) examine the weak- and semi-strong form EMH for the four largest banks listed on Athens Stock Exchange, over the period of January 1985-October 1988. By employing the test developed by Brock, Dechert and Scheinkman (1987) and log daily price data, the results are showing there is neither linear nor nonlinear dependency in the stock price series. Thus, the weak-form EMH is valid. In addition, there is no evidence of cointegration and thus no Granger causality between these stocks. This provides support for the semi-strong form EMH.

Bashir, Ilyas, and Furrukh (2011) found the evidence of market inefficiency for 11 high trading volume bank stocks listed on Karachi Stock Exchange, over the period of June 1997-April 2009. The study employs ADF and PP tests for stationarity check, and co-integration and VAR tests for testing the weak-form EMH. The estimation is applied on bank stock daily closing prices. The observed inefficiency is explained as a consequence of speculative bubbles.

Narayan et al. (2015) examine the weak-form EMH for 34 banking-related stocks from NYSE by using the daily stock price data over the period of 2nd January 1998-31st December 2007. The authors claim that EMH and day-of-the-week hypothesis are interrelated. Thus, they propose the hypothesis that EMH is day-of-the-week dependent. The Augmented Dickey-Fuller (1979) test and Bai and Perron (1998) procedure that allows for the maximum number of breaks for each series indicate that market efficiency is day-of-the-week dependent. The unit root tests applied to each of the five trading days indicate that the null hypothesis of a unit root is rejected for all five trading days, for 21 firms which represent about 62 percent of the whole sample. The overall findings of the study are against the weak-form EMH.

Instead of bank stocks, Chiş (2012) uses sample of study related to the insurance industry. The study explores on the weak-form efficiency of insurance company unit-linked funds. In order to empirically assess the return predictability of eight ING unit-linked funds, the martingale difference hypothesis (MDH) is examined for the period of 21st July 1999-1st June 2012, which posits that stock returns are uncorrelated with their past values. The MDH is rejected for almost all unit-linked fund markets, except for ING Poland Bonds Sub-Fund and ING Poland Balanced Sub-Fund. This implies that most of these markets are yet to achieve the weak-form efficiency.

In Malaysia, Lim et al. (2007) study the random walk behaviour of stock prices using a sample of four bank stocks, namely Hong Leong Bank, Malayan Banking, Public Bank, and Southern Bank. The analysis of study is by employing the log daily returns data for the period of 1st January 1990-30th June 2004. The results of the windowed-test procedure of Hinich and Patterson (1995) show the presence of linear and nonlinear dependencies in the series, but the observed patterns are non-persistent. The findings suggest that the task of designing a profitable trading rule based on these patterns is extremely difficult.

3Data and methodology3.1Datasets

We use balanced datasets of finance stock price series covering the period from 1st January 1997 to 31st December 2014, in which we obtain a total of 4696 observations for each series. The daily closing prices data of individual finance stocks are sourced from Datastream. Out of 34 finance stocks, we select 28 stocks to be included as the sample of study. Five firms are excluded due to the data are unavailable on 1/1/1997, namely, MPHB Capital Berhad (MPHBCAP), Tuneins Holdings Berhad (TUNEINS), Bursa Malaysia Berhad (BURSA), AEON Credit Service (M) Berhad (AEON), and ELK-Desa Resources Berhad (ELKDESA). In addition, Allianz Malaysia Bhd (ALLIANZ) is excluded as the stock of this firm has displayed un-changing prices for long-periods of time.

For estimation, we use the log daily price data of the selected finance stocks. Log transformation is beneficial as it reduces non-normality of data series (Osborne, 2002). As depicted in Figure 1, the log daily price series appear to be subject to several structural changes. We observe wild behaviours for all series around 1997, a time of Asian financial crisis. It seems that all the datasets contain many non-normal observations for which robust tests may be more appropriate than the usual tests.

3.2Methodology3.2.1Panel nonlinear heterogeneous unit root test

We apply the panel nonlinear unit root test developed by Ucar and Omay (2009) for heterogeneous panel, which is in the framework of Kapetanios, Shin, and And Snell (2003)2. This test is written as follows:

Where yit denotes the Panel Exponential Smooth Transition Autoregressive Process (PESTAR(1)) of order one on the time domain t=1,2,…,T for cross section units i=1,2,…,N, assuming that yit follows the DGP with fixed effect or heterogeneous intercept parameter αi. Further, d≥1 represents the delay parameter, and θi>0 indicates the speed of mean reversion for all cross section units. By setting ϕi=0 for all cross section units so that yit has a unit root process in the middle regime and given that d=1, the PESTAR(1) model is derived as Equation (2) below:

Based on Equation (2), testing the nonlinear unit root in panel is to test the null hypothesis θi=1 for all cross section units against θi>0 for some cross section units under the alternative hypothesis. However, γi is not identified under the null hypothesis, thus the null hypothesis cannot be tested directly using Equation (2). We need to apply first-order Taylor series approximation to the PESTAR(1) model around θi=0 for all cross section units. This allows us to obtain the auxiliary regression as written below:

Then, we derive the hypotheses for unit root testing from the regression as translated in Equation (3). The null hypothesis H0:δi=0 for all cross section units implies linear nonstationarity. Whereas, the alternative hypothesis H1:δi

For a fixed T, the below expression is used:

By satisfying the invariant property that for all ti,NL hold for each cross section unit; and the existence of moments by truncating ti,NL distribution in which the individual statistics ti,NL are iid random variables with finite means and variances, the usual normalization of t¯NL statistics have the limiting standard normal distribution as N→∞ such that,

This provides us the Z¯NL statistic critical values.

3.2.2Panel stationarity test with structural breaks and CSD

Our main method for analysis is based on the panel stationarity test developed by Carrion-i-Silvestre et al. (2005) that incorporates multiple structural breaks and exploits the cross-section variations across the series analyzed. We use Equation (1) for the expression of this test:

Where Pi,t represents the price of finance stock i=1,..., N and t= 1,..., T denote time periods; and εi,t is the error term. The dummy variables DPi,k,t and DTi,k,t* are defined as DPi,k,t=1 for t>Tb,ki and 0 otherwise; and DTi,k,t*=t−Tb,ki for t>Tb,ki and 0 otherwise; where Tb,ki denotes the kth date of the break for the ith individual, k =1,…,mi, mi≥1.

The specification in Equation (1) allows for unit-specific intercepts and linear trends in addition to unit-specific mean and slope shifts. The panel stationarity test of Carrion i-Silvestre et al. (2005) tests the null hypothesis of a stationary panel following the Hadri (2000) procedure by using a simple average of the univariate stationarity test in Kwiatkowski et al. (KPSS, 1992). The test statistic is as Equation (2) below:

Where LM(λˆi)=ωˆi−2T−2∑t=1TSˆi,t2 is the univariate KPSS (1992) test for individual i, and Sˆi,t=∑j=1tεˆi,j represents the partial sum process that is obtained using the estimated OLS residuals from Equation (1), with ωˆi−2 being a consistent estimate of the long-run variance of εi,t. We follow the procedure of Kurozumi (2002) and estimate the long-run variance non-parametrically with the bandwidth of the Bartlett kernel fixed. Kurozumi (2002) suggests that the lag selection procedure in Andrews and Monahan (1992) should not be used to calculate the long-run variance for the KPSS test as it may lead to inconsistency in the test.

Where aˆ is the autoregressive parameter estimated with the method proposed by Andrews (1991) and k = 0.7 is the preferred value according to Kurozumi's simulations that maintains a compromise between size and power performance.

Since the test is dependent on the vector λi=λi,1,…,λi,mi'=Tb,1i/T,…,Tb,mii/T' for each i, which indicates the relative positions of the break dates on the whole time period (T), we estimate the vector λi for each unit using the procedure of Bai and Perron (1998) which is based upon the global minimization of the sum of squared residuals (SSR). The procedure is chosen as the location estimation of the breaks for the argument that minimizes the sequence of the unit-specific SSRTb,1i/T,…,Tmi,1i obtained from Equation (1) such that:

After obtaining the dates for all possible mi≤ mmax for each i, where mmax is the maximum number of breaks, we select the appropriate number of structural breaks using the modified Schwarz information criterion (LWZ) of Liu, Wu, and Zidek (1997), which is designed for the case of trending variables. Once the vector λˆi is determined, we compute the normalized test statistic as follows:

Where ξ¯ and ζ¯2 are calculated as the respective averages of the individual means and variances of LMi(λˆi). The computation of the Z(λˆ) statistic requires the individual series to be cross-sectionally independent with asymptotic normality. Since the above assumptions may be overly simplistic, we apply the bootstrap distribution of panel stationary test with multiple breaks following Maddala and Wu (1999). This allows for any kind of cross-sectional dependence and is expected to correct finite-sample bias.

4Empirical results4.1Univariate unit root tests

The analysis based on unit root test method has commenced through traditional univariate unit root tests to provide a benchmark of results. The Augmented Dickey-Fuller (ADF, 1979) and Phillips-Perron (PP, 1988) tests are used to examine the null hypothesis of a unit root (non-stationary) in the log daily price series of each finance stock. Meantime, the Kwiatkowski et al. (KPSS, 1992) unit root test is applied to examine the null hypothesis that a series is stationary. On the basis of traditional univariate unit root tests, mixed-results are provided. As depicted in Table 1 below, when only includes intercept, the null hypothesis of unit root is rejected for KAF, MANULFE, MNRB, ECM, TA, INSAS, and JOHAN at least at the 5 percent level of significance, and KENANGA, P & O, and APEX at the 10 percent level of significance. When includes intercept plus trend in the ADF (1979) and PP (1988) tests, the null hypothesis of unit root is rejected for AMBANK, HLFG, PBBANK, KAF, LPI, MANULFE, and TA at least at the 5 percent level of significance, and AFFIN, RHBCAP, HLBANK, and MNRB at the 10 percent level of significance. The KPSS (1992) test produces very different results when compared with the ADF (1979) and PP (1988) tests. The results of tests for both intercept only and with trend reject the null hypothesis of stationarity for all the series at least at the 5 percent level of significance, except for KAF and KENANGA which the test results for intercept only can reject the null hypothesis at the 10 percent level of significance. Inconsistent with the ADF (1979) and PP (1988) tests, the KPSS (1992) test provides strong evidence showing all the series are random walk processes suggesting the market for these stocks are weak-form efficient. However, the results of univariate unit root tests are for benchmarking.

4.2Conventional panel unit root tests

Traditional univariate unit root tests such as the ADF (1979) and PP (1988) tests are known to have low power against the alternative of stationarity of the series especially when small samples are used. In order to overcome this issue, we employ different conventional panel unit root tests (first generation) which differ in their treatments of the null hypothesis. For examples, the Breitung (2000)t-test and Levin et al. (LLC, 2002) test specify the null as a unit root and assumes common unit root processes. The Im et al. (IPS, 2003)w-test and the ADF and PP Fisher chi-squared tests proposed by Maddala and Wu (1999) specify the null as a unit root but assume individual unit root processes. Further, the Hadri (2000)z-test treats the null as no unit root and assumes common unit root processes.

Table 2 summarizes the results from the conventional panel unit root tests employed. The results are mixed irrespective of whether a trend is included in the specification. With intercept only, the LLC (2002) t-stat suggests non-rejection of the null hypothesis of unit root, but the IPS (2003) w-stat indicates rejection of null hypothesis of unit root at the 1 percent level of significance. Whereas, the Hadri (2000)Z-stat suggests rejection of the null hypothesis of stationarity at the 1 percent level of significance. Thus, only the results of LLC (2002) t-stat and Hadri (2000)Z-stat are consistent. After includes trend, the results of LLC (2002) t-stat and IPS (2003) w-stat strongly reject the null hypothesis of unit root at the 1 percent level of significance. Meantime, Hadri (2000)Z-stat shows the null hypothesis of stationarity can be rejected at the 1 percent level of significance, implying all series are non-stationary and contain a unit root. Thus, the results of both LLC (2002) t-stat and IPS (2003) w-stat are contrasting with Hadri (2000)Z-stat. The mixed-results is due to the negligence of accounting for CSD and structural breaks.

4.3Test for cross section dependence

Until now, the presentation of the panel statistics has assumed that individuals are cross-section independent. Nevertheless, this assumption might be restrictive in practice since the analysis of macroeconomic time series for different financial firms are affected by similar important events that might cause dependence among individuals in the panel dataset. The selected financial firms are interrelated in the domestic financial system. IMF (2014) suggests that these firms are highly interdependence through the domestic wholesale funding market. Thus, it is very likely that these firms are simultaneously affected by common observed shocks such as, the Asian financial crisis in 1997-1998, the global financial crisis in 2008-2009, and changes in oil prices around the time period of 2004-2006. As noted by O’Connell (1998) and Maddala and Wu (1999), conventional panel unit root tests derived under the assumption of cross-sectional independence are subject to large size distortions when a substantial degree of cross-correlation exists. However, panel unit root tests that allow for cross-correlation suffer from power losses in the absence of CSD in the data.

Due to foregoing considerations, we use the CD statistic of Pesaran (2004) to test the cross-section dependence across the finance stock price series. If the presence of common shocks generate dependence among the units in the panel, we need to select panel unit root test which is robust to CSD so that to prevent size distortion of the test. In more details, Pesaran (2004) has proposed a simple test of error CSD which is suitable for both stationary and nonstationary panels under general conditions. The cross section dependence test is based on the average of pair-wise correlation coefficients of OLS residuals obtained from standard ADF (1979) regressions for each individual. Let ρˆij be the sample estimate of pair-wise correlation coefficients of OLS residuals such that:

Where eit represents the OLS estimated residuals for individual i. Based on pair-wise correlation coefficients, the Pesaran (2004) test does not depend on any particular spatial weight matrix as is the case for the Breusch and Pagan (1980) LM test when the cross-sectional dimension (N) is large. The CD statistic in Pesaran (2004) is given by:

The Pesaran's CD statistic tests the null hypothesis of cross-sectional independence and is distributed as a two-tailed standard normal distribution.

4.4Cross-section dependence test results

As depicted in Table 3, the CD statistic of Pesaran (2004) is highly significant for the finance stock price series. The null hypothesis that innovations to the variable are cross-sectional independent is strongly rejected at the 1 percent significance level. Although it is not the case here, a possible drawback of the CD test is that adding up positive and negative correlations may result in failing to reject the null hypothesis even if there is substantial of CSD in the errors. Since the average absolute correlation is 0.527 which is a very high value, therefore there is enough evidence for the presence of CSD in the series. This result is in accordance with our expectation that there is a high level of cross-sectional dependencies across the selected financial firms due to common shocks.

4.5Panel nonlinear heterogeneous unit root test

Nonlinear behaviour of stock prices is well-documented in the literature, therefore the panel nonlinear heterogeneous unit root test developed by Ucar and Omay (2009) is employed to test whether the finance stock price series contain a unit root or not. If the null hypothesis linear non-stationarity cannot be rejected, this will suggest the finance stocks are efficient as a group. On the other hand, this group is inefficient if the null hypothesis is rejected. As reported in Table 4, when we include intercept only, the results show that we cannot reject the null hypothesis of linear non-stationarity. When we include intercept and trend, the results significantly change. The p-values are showing 0.027 which rejecting the null hypothesis at 5 percent level of significance. This implies that the finance stocks as a group are seen to be inefficient in the weak-form. This result may due to the fact that this test has low power against structural break stationary process. In addition, the alternative hypothesis of Ucar and Omay (2009) panel unit root test indicates that at least one series is stationary but it does not provide evidence which series is stationary. Therefore, we apply Carrión-i-Silvestre et al. (2005) panel stationarity test to address the ignorance of structural breaks and the stationary of the specific series.

4.6Panel stationarity test with structural breaks and CSD

Since the series appear to be cross-sectional correlated, we proceed by testing for a unit root using the panel stationarity test advanced by Carrión-i-Silvestre et al. (2005). This method allows for endogenously determined multiple structural breaks, and is flexible enough to control for CSD by accommodating the appropriate critical values by using the bootstrapping procedure.

Table 5 reports the results from Carrión-i-Silvestre et al. (2005) panel stationarity test. The last four columns in Panel A of Table 5 show the computed 10 percent, 5 percent, and 1 percent finite KPSS critical values, by means of Monte Carlo simulations of 20 000 draws. These critical values are used to control for the finite sample bias that might be present in small samples used in the paper. The panel KPSS statistics are clearly larger that the finite sample KPSS 1 percent critical values. Therefore, we reject the null hypothesis of stationarity for all the finance stock price series.

Next, we compare the panel KPSS statistics using the assumptions of homogeneous and heterogeneous variance, with the bootstrapped empirically distributed critical values at the 1 percent, 5 percent, and 10 percent levels of significance. For both the homogeneous and heterogeneous variance assumptions, the actual panel KPSS statistics are greater than the bootstrapped critical values at the 1 percent, 5 percent, and 10 percent levels of significance. Thus, we reject the joint null hypothesis of stationarity. We conclude that, after allows for multiple structural breaks and controls for CSD, the selected finance stock price series are non-stationary and these stocks are weak-form efficient. The findings of Lim et al. (2007), Bashir et al. (2011), and Narayan et al. (2015) are not in favour of the weak-form efficiency particularly when bank stocks are considered. Thus, our results are inconsistent with their findings. Our results match the finding of Stengos and Panas (1992) who found evidence showing bank stocks are weak-form efficient.

Our results from the panel stationarity test of Carrión-i-Silvestre et al. (2005) suggest that there are infrequent large fluctuations in the series studied. The estimated breakpoints are summarized in Table 6. The first observed common breaks in all these series are around the time of September 1999-December 2000 which may correspond to two important policy changes introduced to the domestic financial system. The capital-lock imposed under the September 1998 capital controls aftermath to the Asian financial crisis lapsed on 1st September 1999, as the economy of Malaysia was recovering from the second quarter of 19993. Meantime, the Government announced a major consolidation plan of 71 domestic financial institutions into six banking groups on July 1999, but the plan was frozen in September 1999 and then revised to ten banks in October 1999. The merger process had only completed end of 20014.

Our results indicate that there are other breakpoints in the series. For examples, the break dates around the year 2002 may correspond to the events of global economic downturn and Dotcom crash in 2000-2002, the observed breaks in June-August 2009 may relate to the 2009 flu outbreak (H1N1) in Malaysia, the fluctuations in world crude oil prices in 2004-2006 may explain breaks around this particular period, the US subprime mortgage crisis and Great Recession can explain the drastic changes in the series during 2008-2009, and the period of 2011-2012 corresponds to global economic downturn and the European sovereign debt crisis. These events could have increased the level of risk aversion among stock market investors, causing large changes in stock market investment.

5Summary and conclusion

This paper has examined the weak-form EMH using a sample of 28 finance stocks from the Finance Sector in Malaysian Stock Exchange, and covers the period spanning 1st January 1997 until 31st December 2014. We consider that finance stocks have paramount importance in terms of the effectiveness of capital allocation across different financial industries such as, commercial banking, investment banking, insurance business, capital market intermediation, and finance company business. In addition, financial firms as well as finance stocks are considerable fragile during financial crises. In particular, bank soundness can be affected by the inherent maturity mismatch on a bank's balance sheet. Further, banking sector contagion is more strong and rapid that other sectors (Narayan et al., 2015). To the best of our knowledge, there has been no study examining the empirical validity of the weak-form EMH for finance stock price series by addressing the issues of CSD and structural breaks.

Our results from traditional univariate unit root tests are mixed. For instance, when includes intercept and trend, the results of the ADF (1979) and PP (1988) tests indicate that 11 finance stock price series do not follow a random walk process including AFFIN, AMBANK, HLFG, RHBCAP, HLBANK, PBBANK, KAF, LPI, MANULFE, MNRB, and TA, and the rest of the series are random walk processes. However, the KPSS (1992) test suggests that all the series are non-stationary and contain a unit root, thus providing strong evidence showing the market for these finance stocks is efficient in weak-form sense. As it is known that univariate unit root tests have low power especially when small samples are used, we proceed with conventional panel unit root tests but again contradicting results are obtained. When includes intercept plus trend, only the Hadri (2000)Z-stat indicates the series are non-stationary implying efficiency, but both the LLC (2002) t-stat and IPS (2003) w-stat show the series do not contain a unit root suggesting inefficiency. We suspect the contrasting results obtained so far are due to the negligence of accounting for CSD and structural breaks, therefore we apply the CD statistic of Pesaran (2004) to test for cross section dependence in the series. We found strong CSD among the finance stock series as the Pesaran (2004) CD statistic strongly rejects the null hypothesis of cross section independence (1 percent level of significance). Further, by applying nonlinear heterogeneous panel unit root test of Ucar and Omay (2009), our results suggest that the finance stock series as a group are inefficient in the weak-form sense. In the final step, we employ the panel stationarity test advanced by Carrión-i-Silvestre et al. (2005) that accommodates both structural breaks and CSD. Based on the results of panel KPSS (1992) test using the assumptions of homogeneous and heterogeneous variance, with the bootstrapped empirically distributed critical values at the 1 percent, 5 percent, and 10 percent levels, we reject the joint null hypothesis of stationarity. This result allows us to conclude that all the series are random walk processes, suggesting the market for these finance stocks is weak-form efficient. In addition, important breakpoints in the series are captured which may correspond to the major policy changes in Malaysia and several global events.

The overall findings of this present study suggest that the market for the selected finance stocks in Malaysia are weak-form efficient. In Malaysia, a large portion of shareholdings in the listed financial holding companies and commercial banks are held by the main government-linked institutions, including Boustead Holdings Berhad, Employees’ Provident Fund, Khazanah Nasional Berhad, Lembaga Tabung Angkatan Tentera, Permodalan Nasional Berhad, and Skim Amanah Saham Bumiputera. According to IMF (2014), these institutions held about 40-60 percent of total shareholdings in Affin, CIMB, Maybank, and RHB. The government-linked institutions are typically long-horizon institutional investors. Unlike long-term investors, short-horizon institutional investors tend to sell their holdings substantially during the time of market turmoil (Cella, Ellul, & Giannetti, 2013). This amplifies the shocks to the prices of stocks held by short-horizon investors. However, long-horizon investors are less affected in such condition. Since the government-linked institutions held substantial shareholdings in finance stocks, these stocks are likely to be efficient. Aside from this, the selected finance stocks are efficient because these stocks are attractive to long-term investors.

The finding that the Finance Sector in Malaysian Stock Exchange is weak-form efficient has several important implications. First, the selected financial firms are expected to be able to raise long-term capital through their equity issues. As compared to other stocks, the efficient stocks are more attractive to long-term investors because these stocks provide accurate price signals to guide the decision-making by investors. In other words, the efficient stocks are more liquid allowing investors to buy and sell shares quickly and cheaply. Market efficiency for these finance stocks will enhance the role of the finance sector in stimulating economic growth and transforming Malaysia into a high value-added and high income country by the year 2020.

Second, we foresee that efficiency will contribute to the good prospect of Malaysian commercial banks in complying with the minimum capital requirements set by the International Regulatory Framework for Banks (Basel III), by 1st January 2019. The minimum equity tier 1 of 7 percent equals to minimum equity of 4.5 percent with capital conservation buffer of 2.5 percent. Presently, the banking groups of Malaysia are expected to be able to meet the minimum capital requirements in the low and baseline growth scenarios, but not in the high growth scenario5.

Third, the weak-form efficiency would suggest that any techniques used for predicting stock prices are futile in the long-run. Our findings show that the selected finance stocks are weak-form efficient, suggesting investors are better-off by simply buy-and-hold over long-term investment horizon rather than frequently trade and speculate. Frequent buying and selling will lead to higher transaction costs.

Fourth, if stock prices follow a random walk or unit root process, the impact of shocks to stock prices will be permanent, thus the order of past price changes cannot predict future prices (Narayan & Smyth, 2004; Narayan & Narayan, 2007; Munir et al., 2012). Our findings suggest that shocks to the prices of the selected finance stocks are permanent and investors cannot exploit mean-reversion for prediction.

The scope of weak-form EMH tests is broad including various aspects under the rubric of return predictability that are beyond the random walk model. So far, our findings suggest that the prices of Malaysian finance stocks are characterized as random walk processes. Therefore, we infer that techniques used to predict the future movements in the stock prices are futile in the long-run. Future research may pay attention on the existence of anomalies for the insight of specific investment rules and strategies based on the observed patterns in the finance stock series (i.e. contrarian investment strategy, momentum-based investment strategy, and calendar anomalies).


Thanks are due to the editor of the journal and two anonymous referees for their valuable comments and suggestions. All remaining errors are our own. This research was supported by a project code SBK0201-SS-2015 from the SGPUMS (Universiti Malaysia Sabah).


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Источник: https://www.elsevier.es/es-revista-journal-economics-finance-administrative-science-352-articulo-malaysian-finance-sector-weak-form-efficiency-S207718861500058X
what is capital market efficiency in finance
what is capital market efficiency in finance

: What is capital market efficiency in finance

What is capital market efficiency in finance
What is capital market efficiency in finance

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