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To investigate the performance of moving average trading rules in an emerging market context, namely that of the Jordanian stock market.

The conditional returns on buy or sell signals from actual data are examined for a range of trading rules. These are compared with conditional returns from simulated series generated by a range of models (random walk with a drift, AR (1), and GARCH‐(M)) and the consistency of the general index series with these processes is examined. Sensitivity analysis of the impact of transaction costs is conducted and standard statistical testing is extended through the use of bootstrap techniques.

The empirical results show that technical trading rules can help to predict market movements, and that there is some evidence that (short) rules may be profitable after allowing for transactions costs, although there are some caveats on this.

New results for the Jordanian market; use of sensitivity analysis to investigate robustness to variations in transactions costs.

This paper explores whether workers share innovation returns and how the size of innovation returns is affected by market conditions. Using a panel data of Spanish manufacturing firms during the period from 1990 to 1993, we answer affirmatively to both questions. Product and process innovations both generate returns, but such returns are higher for process innovations. The size of innovation returns seems to be affected positively by demand growth, by product standardization, and by low product market concentration. The three empirical results are in agreement with the theoretical predictions, such as Schmoockler’s (1966) theory of demand‐pool innovation, the price‐elasticity of demand effects postulated by Kamien & Schwartz (1970), and the replacement effect suggested by Arrow (1962). At the time of generating returns, process innovations are more affected by market conditions than are other innovations.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

This paper aims to explore how fund managers (FMs) deal with major problems of ignorance and uncertainty in stock selection and in asset allocation decisions.

Interviews were conducted with 40 fund managers in the period October 1997 to January 2000. A seven stage approach was adopted to sift through and process the large volumes of case data. The interview case data formed the basis for identifying common patterns and themes across the cases.

The case data revealed the nature of this private information agenda concerning intellectual capital or intangibles and the dynamic connections between these variables in the value creation process. The case data provided insight into how the book value and market value gap arose and the special role of information on intangibles and intellectual capital in valuing the company.

The fund management behaviour has important implications for regulatory policy issues on insider information, on corporate disclosure, the corporate governance role of financial institutions, and for the governance of financial institutions.

The paper focuses on issues of importance in an increasingly concentrated and global FM industry.

This study aims to compare the fundamental indexation (FI) portfolio vis-à-vis the cap-weighted index (CWI). It also explored the return-generating attributes of the FI portfolios.

This study extracted relevant data from the Centre for Monitoring Indian Economy’s Prowess database from March 1996 to March 2017 from a sample of National Stock Exchange (NSE) 500 companies. The FI portfolios were constructed with First_50 and Next_50 stocks using the latest and five years of trailing average aggregations. Further, the regression technique was used to identify the return-generating attributes of FI portfolios.

It was found that the FI portfolios based on First_50 and Next_50 stocks outperformed the CWI (i.e. NSE_First_50 and NSE_Next_50) in the Indian capital market, and between the two, the FI portfolios based on Next_50 stocks were superior to the FI portfolios based on First_50 stocks. The cross-sectional superiority of FI portfolios is obvious if they are sorted according to four fundamentals, namely, total income, sales, operating cash flows and profit before depreciation interest tax and amortisation. The return-generating process of FI portfolios is well-explained by market premium followed by value premium and investment premium.

This study may enable portfolio managers and investors to measure FI portfolios’ superiority in the Indian capital market and identify the return-generating attributes of FI portfolios so that the loadings can be switched amongst different priced factors for higher yield. Further, this study extends the FI literature, providing evidence from one of the world’s fastest-growing economies.

To the best of the knowledge, this is amongst the first few studies to explore the performance of FI portfolios vis-à-vis CWIs in India, and to use Fama and French (2015) asset pricing models to understand the return-generating attributes of FI portfolios. It is also novel in the sense that it considers the FI portfolios for a longer duration, predating 1997 and coinciding with the inception of CWIs, namely, NSE_First_50 (inception: 1995) and NSE_Next_50 (inception: 1996), reducing the apprehensions of data-snooping biases.

This chapter considers the relationship between stock market autocorrelation and (i) the presence of international investors which is proxied by the level of capital market integration and (ii) stock market volatility. Drawing from a sample of nine Asia-Pacific stock indices, significant evidence of a relationship between the presence of international investors and the level of stock market autocorrelation is found. This evidence is consistent with the view that international investors are positive feedback traders. Robustness testing of this model suggests that the trading strategy of international investors changed as a result of the Asian currency crisis. The evidence for the role of volatility in explaining autocorrelation is, however, is generally weak and varies across the sample countries.

The purpose of this study is to investigate interest rate sensitivity of the US property/liability (P/L) insurers stock returns using various return generating process models incorporating different interest rate changes such as actual interest rate changes, unexpected interest rate changes and orthogonalized market returns.

The study follows the 1974 two‐index model by Stone. In the two‐index model, three different interest rate indices are tested one at a time to examine if interest rate sensitivity of the insurers stock returns, if any, is vulnerable to an interest rate index used.

It is found that the US P/L insurers' stock returns are sensitivity to interest rate changes. The impact of actual interest rate changes on the stock returns is little different from that of unexpected interest rate changes, which is consistent with findings in the banking literature. When orthogonalized market returns are used in the models in lieu of actual market returns, the statistical significance on the estimated interest rate sensitivity of the returns improves. Consistent with extant studies of financial institution's interest rate sensitivity, the paper also reports that the interest rate sensitivity of insurer stock returns is time varying.

Due to the data availability, the period studied is between 1992 and 2001. However, the sample period does not weaken the findings of the study. In addition, future research could incorporate the insurers' balance sheet items to investigate which balance sheet items (i.e. investment in bonds, stocks and other items on asset side and reserves and other items on liability side) explain most interest rate sensitivity.

Investors can adopt the findings of this study in creating or adjusting their portfolio with the US P/L insurers in it. The insurers stock returns are more sensitive to changes in long‐term interest rate when the underwriting profit increases and the stock returns are more sensitive to changes in short‐term interest rate when the underwriting profit decreases.

Although generalization is difficult and the conclusion is not as convincing as it could be because only one underwriting cycle is sampled, it is still noteworthy to recognize that the insurers' interest rate sensitivity is closely related to the insurance industry's underwriting cycle or performance.

This is the first study reporting the association between the interest rate sensitivity of the US stock returns and the underwriting performance.

The Treynor and Mazuy framework is a widely used return-based model of market timing. However, existing corrections to the regression intercept can be manipulated through derivatives trading. Because they are conceptually flawed, these corrections produce biased performance measures. This paper aims to get back to Henriksson and Merton’s initial idea of option replication to overcome this issue and adapt the market timing model to various kinds of trading strategies and return-generating processes.

This paper proposes a theoretical adjustment based on Merton’s option replication approach adapted to the Treynor and Mazuy specification. The linear and quadratic coefficients of the regression are exploited to assess the cost of the replicating option that yields similar convexity for a passive portfolio. A similar reasoning applies for various timing patterns and in multi-factor models.

The proposed framework induces a potential rebalancing risk and involves the delicate issue of choosing the cheapest option. This paper shows that these issues can be overcome for reasonable tolerance levels. The option replication approach is a workable approach for practical applications.

The adaptation of Merton’s reasoning to the Treynor and Mazuy model has surprisingly never been proposed so far. This paper has the potential to correct for a pervasive bias in the estimation of the performance of a market timer in the context of this very popular quadratic regression setup. Because of the power of the option replication approach, the reasoning is shown to be applicable to multi-factor models, negative timing and market neutral strategies. This paper could fuel empirical studies that would shed new light on the genuine market timing skills of active portfolio managers.

This paper examines the effect of rational and irrational investor sentiment on the stock return and volatility of US auto, finance, food, oil and utility industries.

The American Association of Individual Investors Index (AAII) is used as a proxy for US individual investor sentiment. The US market fundamentals are regressed on investor sentiment in order to capture the effect of macroeconomic risk factors on investor sentiment. Then impulse response functions (IRFs) are generated from a VAR model to investigate the effect of unanticipated movements in US investor sentiment on both industry‐specific stock return and volatility.

The results show a significant impact of investor sentiment on stock return and volatility in all the industries. We find that the positive rational component of US individual investor sentiment tends to increase the stock return in these industries. We also document that unanticipated increase in the rational component of US individual investor sentiment has a significant negative impact only on the industry volatilities of US auto and finance industries.

The results are based only on the 1999 – 2010 US industry‐specific stock return and volatility data and are confined to these industries.

The findings of this paper can help investors to improve their asset return generating models by incorporating investor sentiment. The findings can also help policymakers to design policies that stabilize sentiment and reduce volatility and uncertainty in the stock markets.

This paper adds to the growing literature on behavioral finance by filling a gap and addressing the impact of investor sentiment in the various US industries.

Investors’ fear can be rational, emanating from the natural dynamics of economic fundamentals, or it can be quasi rational and not attributable to any known risk factors. Using VIX from Chicago Board Options Exchange as a proxy for investors’ fear, the purpose of this paper is to consider the following research questions: to what extent does noise play a role in the formation of investors’ fear? To what extent is the impact of fear on S&P 500 index returns driven by rational reactions to new information vs fear induced by noise in stock market returns? To what extent do S&P 500 index returns display asymmetric behavior in response to investor’s rational and quasi rational fear?

In a two-step process, the authors first decompose investors’ fear into its rational and irrational components by generating two additional variables representing fear induced by rational expectations and fear due to noise. The authors then estimate a three-vector autoregression (VAR) model to examine their relative impact on S&P 500 returns.

Impulse responses generated from a 13-variable VAR model show that investors’ fear is driven by risk factors to some extent, and this extent is well captured by the Fama and French three-factor and the Carhart four-factor models. Specifically, investors’ fear is negatively related to the market risk premium, negatively related to the premium between value and growth stocks, and positively related to momentum. The magnitude and duration of the impact of the market risk premium is almost twice that of the impact of the premium on value stocks and the momentum of investors’ fear. However, almost 90 percent of the movement in investors’ fear is not attributable to the 12 risk factors chosen in this study and thus may be largely irrational in nature. The impulse responses suggest that both rational and irrational fear have significant negative effects on market returns. Moreover, the effects are asymmetric on S&P 500 index returns wherein irrational upturns in fear have a greater impact than downturns. In addition, the component of investors’ fear driven by irrationality or noise has more than twice the impact on market returns in terms of magnitude and duration than the impact of the rational component of investors’ fear.

The results are consistent with the view that one of the most important drivers of stock market returns is irrational fear that is not rooted in economic fundamentals.