TACTICAL ASSET ALLOCATION: CAN IT WORK?

Market events of the past ten years have sparked an interest in tactical asset allocation. In the current study we develop and test a model that incorporates currently available information into the tactical asset allocation process. The model provides an estimate of the probabilities that the upcoming market period will be bullish or bearish. Logit analysis is employed to determine which of the various timely and readily available data significantly affect these probabilities. These estimated probabilities are used to suggest the optimal allocations of funds over time between the risk-free asset and the market portfolio. Then, several timing strategies are compared with a buy-and-hold portfolio. An asset allocation strategy based on the probabilities assigned by the logit model appears to achieve greater terminal wealth with less variability of returns. Similar results are obtained for both an initial sample (1962–76 in our model) and a holdout sample (1977–88).     

Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

Profitable candlestick trading strategies—The evidence from a new perspective

This paper aims to investigate the profitability of two-day candlestick patterns by buying on bullish (bearish) patterns and holding until bearish (bullish) patterns occur. Our data set includes daily opening, high, low, and closing prices of component stocks in the Taiwan Top 50 Tracker Fund for the period from 29 October 2002 through 31 December 2008. We examine three bullish reversal patterns and three bearish reversal patterns. We find that three bullish reversal patterns are profitable in the Taiwan stock market. For robustness checks, we evaluate the applicability of our results to diverse market conditions, conduct an out-of-sample test and employ a bootstrap methodology.     

Option pricing model with sentiment

In this paper, we develop a closed-form option pricing model with the stock sentiment and option sentiment. First, the model shows that the price of call option is amplified by bullish stock sentiment, and is reduced by stock bearish sentiment, and the price of put option is in the opposite situation. Second, the price of call option is more sensitive to bullish stock sentiment; the price of put option is more sensitive to bearish stock sentiment. Third, the price of call option increases substantially with respect to the stock sentiment and the option sentiment. The price of put option decreases substantially with respect to the stock sentiment, increases substantially with respect to the option sentiment. Fourth, our models also reveal that the option volatility smile is steeper (flatter) when the stock sentiment becomes more bearish (bullish). Finally, stock sentiment and option sentiment lead to the option price deviating from the rational price. The model could offer a partial explanation of some option anomalies: option price bubbles and option volatility smile.     

Prospect theory–based portfolio optimization: an empirical study and analysis using intelligent algorithms

The behaviourally based portfolio selection problem with investor’s loss aversion and risk aversion biases in portfolio choice under uncertainty is studied. The main results of this work are: developed heuristic approaches for the prospect theory model proposed by Kahneman and Tversky in 1979 as well as an empirical comparative analysis of this model and the index tracking model. The crucial assumption is that behavioural features of the prospect theory model provide better downside protection than traditional approaches to the portfolio selection problem. In this research the large-scale computational results for the prospect theory model have been obtained for real financial market data with up to 225 assets. Previously, as far as we are aware, only small laboratory tests (2–3 artificial assets) have been presented in the literature. In order to investigate empirically the performance of the behaviourally based model, a differential evolution algorithm and a genetic algorithm which are capable of dealing with a large universe of assets have been developed. Specific breeding and mutation, as well as normalization, have been implemented in the algorithms. A tabulated comparative analysis of the algorithms’ parameter choice is presented. The prospect theory model with the reference point being the index is compared to the index tracking model. A cardinality constraint has been implemented to the basic index tracking and the prospect theory models. The portfolio diversification benefit has been found. The aggressive behaviour in terms of returns of the prospect theory model with the reference point being the index leads to better performance of this model in a bullish market. However, it performed worse in a bearish market than the index tracking model. A tabulated comparative analysis of the performance of the two studied models is provided in this paper for in-sample and out-of-sample tests. The performance of the studied models has been tested out-of-sample in different conditions using simulation of the distribution of a growing market and simulation of the t-distribution with fat tails which characterises the dynamics of a decreasing or crisis market.     

Large returns,conditional correlation and portfolio diversification: a value-at-risk approach

This paper, using daily returns on 30 Dow Jones Industrial stocks for the period 1991-1999, investigates the possibility of portfolio diversification when there are negative large movements in the stock returns (i.e. when the market is bearish). We estimate the quantiles of stock return distributions using non-parametric and parametric methods that are widely being used in measuring value-at-risk (VaR). We find that the average conditional correlation of 30 stocks is much higher when the large movements are negative than that when the market is 'usual'. Further, we find that, contrary to the results of previous studies, there is no notable difference between the average conditional correlations when the large movements are positive and when the market is 'usual'. Moreover, it is evident from the results of the conditional CAPM that the portfolio's diversifiable and non-diversifiable risks, as measured by the error variance of the CAPM and beta respectively, are highly unstable when the market is bearish than that when it is 'usual' or bullish. The overall results suggest that the possibility of portfolio diversification would be eroded when the stock market is bearish. These findings have implications for portfolio diversification and risk management in particular and for finance in general. The ideas presented in this paper can be utilized for testing contagion in the international financial markets, a much-researched topic in international finance.     

Market States and Initial Returns: Evidence from Taiwanese IPOs

This paper investigates the different affecting patterns of the determinants of initial returns under different market states for Taiwanese IPOs. Contrary to the prior literature, this paper estimates the sample separated from different market states, including bullish, bearish, and range-bound markets, and finds that the affecting patterns of the determinants of initial returns indeed exhibit some significant differences under different market states. For instance, the stronger the auditor reputation effect, the lower are the initial returns under a range-bound market, and the market momentum effect is stronger under a bullish market. In addition, the risk perception effect is stronger under a bearish market. These findings show that the empirical result of dividing market states will provide more insights and a greater variety of information as investors make decisions.     

An algorithmic trading system based on a stacked generalization model and hidden Markov model in the foreign exchange market

The Forex market has been one of the most attractive markets to researchers, funds, and traders. Literature shows that a single model algorithm usually cannot perform satisfactorily on the foreign exchange (Forex) time series because of the market's complexity. This study develops an algorithm based on two stacked generalization models consisting of four machine-learning models. First, the optimal lags of features are found using the Fisher discriminant ratio and partial autocorrelation function. Second, one stacked model fits the bullish trends, and the other holds the bearish trends resulting from a hidden Markov model. We reinforce the predictive signals of these models by extracting relationships between currency pairs with correlation and mutual information. Lastly, the proposed algorithm constructs a portfolio based on the strength of signals dependent on correlation and mutual information. As a result, this paper compares the performance of the proposed approach with different states of the model and several established benchmarks regarding return and risk metrics. The outcomes show that the proposed model's added features—such as time series clustering, considering a range of inputs, and internal relationships among different assets—can increase its performance in the Forex market.     

Imitation and contrarian behaviour: hyperbolic bubbles,crashes and chaos

Abstract

Imitative and contrarian behaviours are the two typical opposite attitudes of investors in stock markets. We introduce a simple model to investigate their interplay in a stock market where agents can take only two states, bullish or bearish. Each bullish (bearish) agent polls m ‘friends’ and changes her opinion to bearish (bullish) if (i) at least mρ _hb (mρ _bh ) among the m agents inspected are bearish (bullish) or (ii) at least mρ _hh >mρ _hb (mρ _bb >mρ _bh ) among the m agents inspected are bullish (bearish). The condition (i) ((ii)) corresponds to imitative (antagonistic) behaviour. In the limit where the number N of agents is infinite, the dynamics of the fraction of bullish agents is deterministic and exhibits chaotic behaviour in a significant domain of the parameter space {ρ _hb ,ρ _bh ,ρ _hh ,ρ _bb ,m}. A typical chaotic trajectory is characterized by intermittent phases of chaos, quasi-periodic behaviour and super-exponentially growing bubbles followed by crashes. A typical bubble starts initially by growing at an exponential rate and then crosses over to a nonlinear power-law growth rate leading to a finite-time singularity. The reinjection mechanism provided by the contrarian behaviour introduces a finite-size effect, rounding off these singularities and leads to chaos. We document the main stylized facts of this model in the symmetric and asymmetric cases. This model is one of the rare agent-based models that give rise to interesting non-periodic complex dynamics in the ‘thermodynamic’ limit (of an infinite number N of agents). We also discuss the case of a finite number of agents, which introduces an endogenous source of noise superimposed on the chaotic dynamics.     

Futures Market Sentiment and Institutional Investor Behavior in the Spot Market: The Emerging Market in Taiwan

This paper investigates whether and how futures market sentiment and stock market returns heterogeneously affect the trading activities of institutional investors in the spot market in Taiwan. Our empirical results suggest that foreign investors are net sellers whenever futures market sentiment is bullish and net buyers when investor sentiment is bearish. The two types of domestic institutional investors have poor sentiment timing abilities and the price-pressure effect may account for the behavioral differences among institutional investors. In addition, all three institutional investors are momentum traders. Nevertheless, the momentum trading of foreigners is consistent with an information-based model and that of two local institutional investors, as behavior-based models suggest. This indicates that the same trading momentum strategy can lead to different outcomes for different investors, and both information- and behavior-based momentum trading can exist contemporaneously in the Taiwanese stock market.     

Extreme Correlation of Stock and Bond Futures Markets: International Evidence

This study explores time‐varying extreme correlation of stock–bond futures markets in three major developed countries. In the United States and the United Kingdom, there is evidence of positive extreme stock–bond correlation when both futures markets are extremely bullish or bearish. In Germany, stock–bond futures extreme correlation is negative, suggesting the most diversification potentials of bond futures when German stock index futures market plunges. Macroeconomic news, the business cycle, and the stock market uncertainty all significantly affect the median stock–bond futures correlation. However, only the stock market uncertainty still significantly affects the extreme stock–bond futures correlation when the stock market is extremely bearish.     

Differences of Opinion, Short-Sales Constraints, and Market Crashes

We develop a theory of market crashes based on differences ofopinion among investors. Because of short-sales constraints,bearish investors do not initially participate in the marketand their information is not revealed in prices. However, ifother previously bullish investors bail out of the market, theoriginally bearish group may become the marginal "support buyers,"and more will be learned about their signals. Thus accumulatedhidden information comes out during market declines. The modelexplains a variety of stylized facts about crashes and alsomakes a distinctive new prediction–that returns will bemore negatively skewed conditional on high trading volume.     

Quantile dependencies and connectedness between the gold and cryptocurrency markets: Effects of the COVID-19 crisis

This paper examines the quantile dependence, connectedness, and return spillovers between gold and the price returns of leading cryptocurrencies, using quantile cross-spectral, the return spillovers based the quantile VAR, and quantile connectedness approaches. The results show that the dependencies within cryptocurrencies are highly symmetric and sensitive to different quantile arrangements. Under normal market conditions, we find a high positive dependence within cryptocurrencies and a low positive dependence between cryptocurrencies and gold. The dependence is higher at long term than intermediate- and short- terms before the pandemic during bearish market conditions. In contrast, the degree of dependence decreases at the intermediate- and long-terms during COVID-19 period than before. Moreover, the magnitude of return spillovers is higher at lower quantile (bearish market) than upper quantile (bullish market). Gold serves as a safe haven and diversifier asset for cryptocurrencies during COVID-19 outbreak at both intermediate and long terms.     

Investor Sentiment and Option Prices

This paper examines whether investor sentiment about the stockmarket affects prices of the S&P 500 options. The findingsreveal that the index option volatility smile is steeper (flatter)and the risk-neutral skewness of monthly index return is more(less) negative when market sentiment becomes more bearish (bullish).These significant relations are robust and become stronger whenthere are more impediments to arbitrage in index options. Theycannot be explained by rational perfect-market-based optionpricing models. Changes in investor sentiment help explain timevariation in the slope of index option smile and risk-neutralskewness beyond factors suggested by the current models.     

The Effectiveness of Price Limits and Stock Characteristics: Evidence from the Shanghai and Shenzhen Stock Exchanges

We examine the effectiveness of price limits on Chinese A shares and investigate the characteristics of those stocks that hit their price limits more frequently. We find that the effect of price limits is asymmetric for the A shares in upward and downward price movements and different for bullish and bearish sample periods. During a bullish period price limits effectively reduce stock volatility for downward price movements, but not for upward price movements; while during a bearish period price limits effectively reduce stock volatility for upward price movements, but not for downward price movements. Second, price limits delay efficient price discovery for upward price movements, but not for downward price movements. However, we do not find evidence to suggest that price limits harmfully interfere with the stock trading processes in the Chinese A share markets. Finally, we find that actively traded stocks hit their price limits more often and tend to hit the lower limit more frequently when overall market conditions are bearish. Stocks with high book-to-market values of equity hit their upper price limits more frequently, while stocks with a high ratio of tradable shares tend to hit their price limits less frequently.JEL Classification: G10, G14, G15     

A Better Asymmetric Model of Changing Volatility in Stock and Exchange Rate Returns: Trend-GARCH

Abstract

The impact of short run price trending on the conditional volatility is tested empirically. A new family of conditionally heteroscedastic models with a trend-dependent conditional variance equation: The Trend-GARCH model is described. Modern microeconomic theory often suggests the connection between the past behaviour of time series, the subsequent reaction of market individuals, and thereon changes in the future characteristics of the time series. Results reveal important properties of these models, which are consistent with stylized facts found in financial data sets. They can also be employed for model identification, estimation, and testing. The empirical analysis supports the existence of trend effects. The Trend-GARCH model proves to be superior to alternative models such as EGARCH, AGARCH, TGARCH OR GARCH-in-Mean in replicating the leverage effect in the conditional variance, in fitting the news impact curve and in fitting the volatility estimates from high frequency data. In addition, we show that the leverage effect is dependent on the current trend, i.e. it differentiates between bullish and bearish markets. Furthermore, trend effects can account for a significant part of the long memory property of asset price volatilities.     

Nonlinear relationship between monetary policy and stock returns: Evidence from the U.S.

This paper studies the relationship between monetary policy and stock market return in the U.S. using nonlinear econometric models. It first employs a univariate Markov-switching model on each of the three stock indices and three monetary policy variables, displaying significant regime-switching patterns and common movements. This paper then uses a Markov-switching dynamic bi-factor model to simultaneously extract two latent common factors from stock indices and monetary policy variables to represent monetary policy changes and stock market movements separately. The smoothed probabilities of regimes demonstrate that expansionary monetary policy regimes follow economic recessions, but bear stock markets usually occur before economic recessions. The maximum likelihood estimation results show that expansionary monetary policy such as a decrease in the federal funds rate raises stock returns, but stock returns don't directly influence monetary policy decision.     

Model risk of risk models

This paper evaluates the model risk of models used for forecasting systemic and market risk. Model risk, which is the potential for different models to provide inconsistent outcomes, is shown to be increasing with market uncertainty. During calm periods, the underlying risk forecast models produce similar risk readings; hence, model risk is typically negligible. However, the disagreement between the various candidate models increases significantly during market distress, further frustrating the reliability of risk readings. Finally, particular conclusions on the underlying reasons for the high model risk and the implications for practitioners and policy makers are discussed.     

Spending rules for endowment funds

Endowment fund managers face an asset allocation problem with several particularities: they are more interested in spending for current and future beneficiaries than growing value, although the trade-off between these two alternatives needs to be understood; they have to consider longest-term investment, typically an infinite horizon. We do address these allocation constraints in a dynamic framework where minimum subsistence levels (introducing the idea that a minimum spending amount needs to be made at every time period) are introduced in the objective function. We derive explicit formulas for the optimal spending stream, endowment value, spending rate and portfolio strategy in a simple Black/Scholes type economy. We analyze the effects of parameter changes on asset allocation decisions and provide simulations on bearish, median and bullish paths.     

International Evidence on Institutional Trading Behavior and Price Impact

This study characterizes institutional trading in international stocks from 37 countries during 1997 to 1998 and 2001. We find that the underlying market condition is a major determinant of the price impact and, more importantly, of the asymmetry between price impacts of institutional buy and sell orders. In bullish markets, institutional purchases have a bigger price impact than sells; however, in the bearish markets, sells have a higher price impact. This differs from previous findings on price impact asymmetry. Our study further suggests that price impact varies depending on order characteristics, firm‐specific factors, and cross‐country differences.