Small Firm Effect Research Articles

The Return of the Size Anomaly: Evidence from the German Stock Market

This paper examines the size-effect in the German stock market and intends to address several unanswered issues on this widely known anomaly. Unlike recent evidence of a reversal of the size anomaly we document a conditional relation between size and returns. We also detect strong momentum across size portfolios. Our results indicate that the marginal effect of firm size on stock returns is conditional on the firm’s past performance. We use an instrumental variable estimation to address Berk’s critique of a simultaneity bias in prior studies on the small firm effect and to investigate the economic rationale behind firm size as an explanatory variable for the variation in stock returns. The analysis in this paper indicates that firm size captures firm characteristic components in stock returns and that this regularity cannot be explained by differences in systematic risk.

Convergence to Market Efficiency of Top Losers

We use top losers to examine convergence speed of order imbalance on stock return since order imbalance has been claimed to be a state variable in cross sectional return. We found that significance of order imbalance coefficients decreased with increasing time interval (5, 10 and 15-min), indicating evidences on convergence to market efficiency. We also employ a time varying GARCH model to examine the relation between order imbalance and volatility. Again, the significance of order imbalance coefficients shows a decay pattern which also supports convergence to market efficiency hypothesis. We suspect that small firm effect plays a role in convergence. However, our empirical results do not suggest an expected negative relation between order imbalance coefficients and market capitalization. Finally, we developed an imbalance-based trading strategy and made profit from these daily top losers. We short sell seller-initiated order imbalances and buy back buyer-initiated order imbalances. We try many scenarios in testing our strategy. All of them outperformed buy and hold strategy. In order to explain the profitability of order imbalance based strategy, we examine the causal relationship between return and order imbalance. We find that order imbalance is a good indicator of price discovery under our nested causality framework.

A Note on the Pricing of Liquidity in Stock Returns

Keynes (1930) proposed that an asset is more liquid than another “if it is more certainly realisable at short notice without loss” (vol. II, p. 67). This definition suggests that the liquidity of an asset is twofold. First, an asset should have a market that can readily absorb the sale, and second, do so without risk to its final value. This suggests that investors should be rewarded for both the level of liquidity and liquidity risk. The standard form of asset pricing models assumes financial markets to be perfectly liquid. In a perfectly liquid market, there are no arbitrage possibilities. Therefore, the under traditional asset pricing approach, all assets that have similar expected cash flows must have the same price. This phenomenon of frictionless markets ignores the impact of liquidity of financial assets on their respective prices and consequently on returns. The relation between liquidity and expected returns has been statistically observed and explains certain market anomalies such as the small firm effect, equity premium, and risk-free rate puzzle. In a market with frictions, one source of illiquidity is transaction costs, which are ignored in the traditional asset pricing framework. Such costs might include brokerage fees, order processing costs, etc. Whenever a security is traded, the buyer and seller incur transaction costs. Moreover, the buyer will bear additional transaction costs whenever the security is further sold in the market. Apart from transaction costs, other sources of liquidity could be demand pressure and inventory risk. Demand pressure can be created in a market where buyers are not available, and to liquidate the position, the seller might have to settle for a much lower price. The factor of demand pressure might be worsened in the presence of circuit breakers in a continuously bearish market. If the prices hit the lower circuit, sellers will not be able to lay off their positions and this phenomenon will continue if, on the following days, prices continue to open on their lower

Dynamic Intraday Relations between Order Imbalance, Volatility and Return of Jump Losers

This study adopts intraday return instead of daily return used by previous researches to examine the effect of order imbalance not only on the individual stock return but also volatility among jump losers. We also build up order imbalance-based trading strategies to earn abnormal return.A contemporaneous order imbalance-return relation is examined by GARCH (1,1) model and time-series regression model. The data presents significantly positive relation in both models as previous studies on daily reurn. We focus on the lagged effect of imbalance on return and find that such relation is negatively significant, while contemporaneous imbalance has positive significant impact on return. We examine the volatility-order imbalance relationship by a time-varying GARCH (1,1) model. The positive relation of volatility and order imbalance is consistent with our ex ante expectatio n that larger imbalance make s return more volatile. Then, the small firm effect shows the weakly negative relation between order imbalance coefficient and market capitalization.We develop two order imbalance-based trading strategies based on different definitions of price: trading price and bid-ask. Due to the characteristics of our jump losers, we use short selling strategy. Our results show the huge profitability of order imbalance strategies when we trade on extreme volume.In order to explore the reason of profitable order imbalance based strategy, we investigate the causal relationship between return and order imbalance. We find that order imbalance is a good indicator of price discovery. Moreover, order imbalance apparently is an excellent indicator for return prediction in small firm size quartile.

Updated stock index and market seasonals (SIMS) 3.0 data base includes two international control measures

This article provides updated information related to a database that regulators have found useful. Previous versions of the SIMS database were discussed in [Cataldo, A. J. (1998). The stock index and market seasonals (SIMS) data base. Research in Accounting Regulation, 12, 201–221; Cataldo, A. J. (1999). UPDATE – The stock index and market seasonals (SIMS 2.0). Research in Accounting Regulation, 13, 179–180; Cataldo, A. J. (2006). SIMS 2.0 includes controls for NYSE special closings, small firm effects and liquidity. Research in Accounting Regulation, 18, 223–234]. New to the database are BBA LIBOR measures (1997 through September 2007) and Shanghai Composite (SSE) Index measures (2000 through September 2007). BBA LIBOR measures complement the 30-day commercial paper rates provided in SIMS 2.0, as it represents the prevalent measure associated with both debt and equity markets, as impacted by sub-prime loans. The SSE Index was the Asian index most responsive to rumors of the imposition of a capital gains tax in China in late February 2007. The significance of these additional, international debt and equity control variables may require control by capital markets researchers and is the motivation for their inclusion in this description of the expanded SIMS 3.0 data base.

The Mathematics of Idiosyncratic Volatility and the Small Firm Effect: Australian Evidence

In the context of Australian stockmarkets, we examine the relationship between a stock’s return performance, the stock idiosyncratic volatility, and the firm’s market capitalization. The paper’s main conclusions may be summarized as follows. The stocks of the smallest firms markedly outperform the largest capitalized stocks, and for such small capitalized stocks, those with greater idiosyncratic volatility have markedly superior returns. It appears that the relationship of higher returns with higher idiosyncratic volatility is consistent with the mathematics of idiosyncratic volatility. In which case, the small size effect may also be interpreted as the mathematical outcome of idiosyncratic volatility. The paper further examines the condition on which the higher returns reported for either small firm size or high idiosyncratic volatility are likely to be wealth-forming. Finally, we observe that the high performances of the stocks of the smallest firms are likely irrelevant to the class of firms that are of interest to the institutional investor.

The Return of the Size Anomaly: Evidence from the German Stock Market

This paper examines the size-effect in the German stock market and intends to address several unanswered issues on this widely known anomaly. Unlike recent evidence of a reversal of the size anomaly we document a conditional relation between size and returns. We also detect strong momentum across size portfolios. Our results indicate that the marginal effect of firm size on stock returns is conditional on the firm’s past performance. We use an instrumental variable estimation to address Berk’s critique of a simultaneity bias in prior studies on the small firm effect and to investigate the economic rationale behind firm size as an explanatory variable for the variation in stock returns. The analysis in this paper indicates that firm size captures firm characteristic components in stock returns and that this regularity cannot be explained by differences in systematic risk.

The Small Firm Effect and the Entrepreneurial Spawning of Scientists and Engineers

Scientists and engineers in small firms are far more likely than their large firm counterparts to enter entrepreneurship. We label this phenomenon the small firm effect and explore its origins. In particular, we identify four classes of explanations for the small firm effect—preference sorting, ability sorting, opportunity cost, and the possibility that workers in small firms develop entrepreneurial human capital—and examine the empirical evidence for each. We find that preference sorting does play a role in generating the small firm effect: small firms attract those with prior preferences for autonomy who are similarly drawn into entrepreneurship. Similarly, ability sorting plays a role: those who ultimately become entrepreneurs may be drawn first to small firms because they offer tighter pay-for-performance links and can subsequently improve their expected earnings by becoming entrepreneurs, or because the skills required for success in small firms are also valuable in entrepreneurship. Evidence suggests that although those with the very least to lose do enter entrepreneurship with greater frequency, opportunity cost has at best a modest role to play in explaining the small firm effect. Finally, we interpret evidence that prior experience in small firms predicts positive performance outcomes in the early stages of entrepreneurship as suggesting that workers in small firms may develop entrepreneurial human capital that makes them better entrepreneurs. This effect may be largest among those of high ability.

The Small Firm Effect and the Entrepreneurial Spawning of Scientists and Engineers

Scientists and engineers in small firms are far more likely than their large firm counterparts to enter entrepreneurship, a phenomenon we label the small firm effect. We explore the origins of this small firm effect, identify four classes of explanations - preference sorting, ability sorting, opportunity cost, and the possibility that workers in small firms develop entrepreneurial human capital - and exploring these empirically, by examining the determinants of entrepreneurial entry and performance. We find that preference sorting plays a role in generating the small firm effect: small firms attract those with prior preferences for autonomy, who are similarly drawn into entrepreneurship. Similarly, ability sorting plays a role: those who ultimately become entrepreneurs may be drawn to small firms because the offer tighter pay-for-performance links and are drawn to entrepreneurship for the same reason, or because the skills required for success in small firms are also valuable in entrepreneurship. Evidence suggests that, while those with the very least to lose enter entrepreneurship with greater frequency, opportunity cost has at best a small role to play in explaining the small firm effect. Finally, we interpret evidence that prior experience in small firms predicts positive performance outcomes in the early stages of entrepreneurship as suggesting that employment in a small firm also appears to make these workers better entrepreneurs. This effect may be largest among those of high ability.

Is There a Firm-Size Effect in CEO Stock Option Grants?

Schaefer (1998) and Baker and Hall (2004) posit a firm size effect for regular executive compensation but not specifically for executive stock option grants. They propose an inverse relation between pay-performance sensitivity and firm size along with a positive relation between the marginal productivity of executive effort and firm size. The product of pay-performance sensitivity and executive productivity is 'incentive strength'. They find a weakly positive association between incentive strength and firm size. We substitute Hall and Murphy's (2002) pay-performance sensitivity metric to detect a firm size effect in CEO stock option grants. After adjusting for small-firm risk aversion and private diversification 'clienteles', we document evidence of a residual small-firm effect impacting on incentive strength principally through grant size. Given lower small-firm deltas, grant size appears to have been increased by compensation committees to ensure small-firm CEOs are not under-compensated relative to their large-firm counterparts. We also find that firm complexity influences pay-performance sensitivity as well, but not labor productivity (proxying for CEO productivity). No evidence is found that firm smallness and complexity impact on labor productivity. However, we empirically confirm a negative relation between pay-performance sensitivity and firm smallness and, by implication, firm complexity.

Is there a firm-size effect in CEO stock option grants?

Schaefer (1998) and Baker and Hall (2004) posit a firm size effect for regular executive compensation but not specifically for executive stock option grants. They propose an inverse relation between pay-performance sensitivity and firm size along with a positive relation between the marginal productivity of executive effort and firm size. The product of pay-performance sensitivity and executive productivity is „incentive strength‟. They find a weakly positive association between incentive strength and firm size. We substitute Hall and Murphy‟s (2002) pay-performance sensitivity metric to detect a firm size effect in CEO stock option grants. After adjusting for small-firm risk aversion and private diversification „clienteles‟, we document evidence of a residual small-firm effect impacting on incentive strength principally through grant size. Given lower small-firm deltas, grant size appears to have been increased by compensation committees to ensure small-firm CEOs are not under-compensated relative to their large-firm counterparts. We also find that firm complexity influences pay-performance sensitivity as well, but not labor productivity (proxying for CEO productivity). No evidence is found that firm smallness and complexity impact on labor productivity. However, we empirically confirm a negative relation between pay-performance sensitivity and firm smallness and, by implication, firm complexity.

Return‐based investment strategies in the New Zealand stock market: momentum wins

PurposeThe purpose of this paper is to examine the profitability of return‐based investment strategies in the New Zealand stock market; 16 such strategies are examined for the period from January 1995 to December 2004.Design/methodology/approachThe paper shows that, at the end of each month of the sample period, every security is ranked in ascending order using their past J‐month formation period cumulative return (J=3, 6, 9 and 12). Then these securities are allocated to three groups; group 1 represents the loser portfolio, while group 3 represents the winner portfolio. Finally, equally weighted average returns of winner and loser portfolios are calculated over the next K‐month holding period (K=3, 6, 9, and 12). The statistical significance of the returns earned from buying winners and shorting losers is tested in order to determine the profitability of proposed strategies.FindingsThe findings in this paper show that a strong momentum effect, rather than a reversal effect, is present in this market. For example, the strategy, which is based on a six‐month portfolio formation period and a six‐month holding period, generates a monthly return of 1.14 per cent. These strategies are most profitable when they are based on formation and holding periods of three‐to‐six months. Further analyses reveal that the profits generated by such investment strategies cannot be explained by either the small firm effect or the January effect.Originality/valueThe main implication of this paper shows that buying past winners and selling past losers is profitable in the short to medium run in New Zealand.

Investor overreaction to going concern audit opinion announcements

The finance literature extensively documents the existence of stock market anomalies, such as the January effect, the day of the week effect and the small firm effect. Many of these anomalies were discovered or clarified while investigating what has come to be known as the overreaction hypothesis. This paper examines investor overreaction to going concern audit opinion announcements made in the major financial press. The evidence presented suggests the sell-off by investors on the announcement date is followed by a major buy-back of the announcing firms’ shares over the next few days. For the 79 announcing firms in the sample spanning 1984 to 1996, nearly 70% of the average losses on the announcement date are recovered the five days following going concern audit opinion announcements.

Small Firm Effect, Liquidity and Security Returns

Standard asset pricing models ignore the costs of liquidity. In this study we advance the ongoing debate on empirical asset pricing and test if liquidity costs (as proxied by turnover rate, turnover ratio and bid-ask spread) affect stock returns for Australian stocks. Our tests use the factor portfolio mimicking approach of Fama and French (1993, 1996). We find small and less liquid firms generate positive risk premia after controlling for market returns and firm size. We find no evidence of any seasonal effects that can explain our multifactor asset pricing model findings. In summary, our study provides support for a broader asset pricing model with multiple risk factors.

Does the January Effect or April Effect Exist when Value Weighted Industry Indexes are Used in Order to find it? Some Evidence from the London Stock Exchange

Our research examines the tax-year-end effect in the UK stock market by using value-weighted industry indexes. The results provide with evidence that there is no such a seasonal pattern when using this set of data. This fact is likely to lead to the following logical conclusions. Firstly, the tax-year-end effect is not a large-cap firm story. This finding primary supports a plethora of other researches supporting the direct relationship between tax-year-end effect and small firm effect. It seems that large capitalization corporations usually do well during the fiscal year. Therefore, there is no need for investors to sell large-cap stocks in the last trading days of the fiscal year and then buy them back in the first trading days of the new tax-year. Clearly, our analysis provides with some evidence that there is no relationship between large-cap firms and tax-loss selling hypothesis which seems to be responsible for the tax-year-end phenomenon. Additionally, even if this happens, large representative firms have a high level of liquidity and make arbitrageurs' job easier.

Disappearing anomalies: a dynamic analysis of the persistence of anomalies

This study examines several well-known stock market anomalies before and after they were published. If the anomalies are a result of data snooping or data crunching, these are expected to disappear in the data soon after they have been reported. Moreover, increased awareness of anomalies among investors will diminish possible profits as more investors will trade based on these anomalies. Employing dynamic analyses, strong evidence is found that the weekend effect, the holiday effect, the time-of-the-month effect and the January effect have disappeared after these anomalies have been published. The turn-of-the-month effect seems still present, whereas the small firm effect has recently resurrected. The timing of disappearing or reappearing anomalies typically often coincides with the timing of academic publications.

The size effect reversal in the USA

The paper examines the size effect reversal in the USA over the period 1970–1999, using data for the ten size deciles in the CRSP tapes during this 40-year period. Betas for small-firm portfolios increase as the return interval analysed increases, and are lower than large-firm portfolios for daily data but higher for monthly and quarterly data. Differences between small- and large-firm portfolio returns are associated with higher betas as return intervals increase, with lower betas for daily data, and higher for quarterly data. Before 1981 when the small-firm effect was published, smaller firms’ relative risk coefficients were biased downwards compared to aggregated coefficients, while larger firms’ were biased upwards, as expected. But after 1981, a partial reversal occurred with larger firms’ relative risk coefficients also biased downwards and by more than the smaller firms. In the post-period, relative risk measures generated higher abnormal returns for large firms than for small firms, effectively a ...

Seasonal, Size and Value Anomalies

Recent international evidence shows that in many stock markets, general index returns are significantly higher during winter months than during summer months. We study the interaction between this anomaly - known as the Halloween effect - and the January effect and other well-known anomalous findings on portfolios formed on Size, Dividend Yield, Book to Market ratios, Earnings Price ratios and Cash Flow Price ratios in equally but also value weighted portfolios for the US market. Our main findings are that contrary to the January effect, the Halloween effect seems a market wide phenomenon unrelated to these well-known anomalies. All portfolios in our study show higher average winter returns than summer returns. In most portfolios this difference is statistically and economically significant. We confirm recent results which suggest that the January effect plays an important role not only in explaining the small firm effect but also - together with size - in explaining the Book to Market ratio anomaly. In addition, we find in a similar fashion that controlling for the January effect and using value weighted portfolio returns substantially reduces the Earnings to Price, Cash Flow to Price and Dividend Yield effects.

Does Poor Diversification Explain the Small-Firm Effect?

Does Poor Diversification Explain the Small-Firm Effect?

An Investigation of the Value Premium in the UK Stock Market 1988-2002: Some New Evidence

The value premium in the UK stock market for the period 1988-2002 is investigated using a new, survivorship-bias-free, look-ahead-bias-free database, SFD Capital UK Fully Listed. A series of classification and regression tests are carried out, following a similar methodology to Lakonishok, Shleifer & Vishny, 1994. The main findings are (1) an economically significant value premium exists, although smaller than that reported in previous UK studies, (2) value is less risky than growth, using the standard risk measures beta and standard deviation, and (3) a powerful small firm effect persists in the UK market during 1988 to 2002.