INTEGRATED METAHEURISTIC OPTIMIZATION OF 130–30 INVESTMENT‐STRATEGY‐BASED LONG–SHORT PORTFOLIOS

A 130–30 strategy is an attractive and viable equity investment strategy for building long–short portfolios and notionally expected to enhance investment exposure and market protection. However, the amalgamation of the strategy in the portfolio optimization problem model poses complex constraints which render the problem model difficult for solving using traditional methods, thus justifying the application of metaheuristic solutions. We discuss a metaheuristic and integrated optimization of long–short portfolios, when the 130–30‐strategy‐based constraint, besides other investor preferential constraints, is incorporated in the problem's formulation. In the absence of reported work and for reasons of performance comparison and analysis, two metaheuristic strategies have been proposed in order to solve the problem: (i) evolution strategy with hall of fame and (ii) differential evolution (rand/1/bin) with hall of fame. The experimental studies were undertaken on the Bombay Stock Exchange (BSE200) and Tokyo Stock Exchange (Nikkei 225) data sets and specifically for the period March 1999–March 2009, which included both upturns and downturns in the global markets. The efficiencies of the portfolios obtained by the two metaheuristic methods were analysed using an efficiency improvement possibility function, a portfolio productivity indicator which is a variation of Luenberger's shortage functions. Copyright © 2012 John Wiley & Sons, Ltd.     

Investment portfolio balancing: application of a generic self-organizing fuzzy neural network (GenSoFNN)

In contrast to short-term stock trading, portfolio managers are interested in the medium- to long-term peaks and troughs of the stock price cycles as signals to balance their stock portfolios – the predicted trough is the signal to buy the stock and the predicted peak is the signal to sell the stock. As statistical models are generally inadequate or incapable of providing such portfolio balancing signals, we propose using the generic self-organizing fuzzy neural network (GenSoFNN)—a fuzzy neural system – as a tool for portfolio balancing. The network adopts the supervised learning approach to detect inflection points in the stock price cycles, and a modified locally weighted regression algorithm is employed to smooth the stock cycles. The GenSoFNN-based portfolio balancing system was evaluated with experiments conducted using 23 stocks from the New York Stock Exchange and NASDAQ, and the results showed an average profit return of 65.66%. The contributions of the proposed GenSoFNN intelligent portfolio balancing system are twofold: it can be used as an efficient trading solution and it can provide decision support in trading via its generated rules. Copyright © 2009 John Wiley & Sons, Ltd.     

Index tracking with constrained portfolios

Passive portfolio management strategies, such as index tracking, are popular in the industry, but so far little research has been done on the cardinality of such a portfolio, i.e. on how many different assets ought to be included in it. One reason for this is the computational complexity of the associated optimization problems. Traditional optimization techniques cannot deal appropriately with the discontinuities and the many local optima emerging from the introduction of explicit cardinality constraints. More recent approaches, such as heuristic methods, on the other hand, can overcome these hurdles. This paper demonstrates how one of these methods, differential evolution, can be used to solve the constrained index-tracking problem. We analyse the financial implication of cardinality constraints for a tracking portfolio using an empirical study of the Down Jones Industrial Average. We find that the index can be tracked satisfactorily with a subset of its components and, more important, that the deviation between computed actual tracking error and the theoretically achievable tracking error out of sample is negligibly affected by the portfolio's cardinality. Copyright © 2007 John Wiley & Sons, Ltd.     

Seasonal variations in the U.S. Stock Market returns: 1927–1984

When the seasonal components of the monthly returns as opposed to the returns themselves, are examined over the 1927–1984 period, the Standard & Poor's 500 Composite Index (S&P 500) and the Center for Research in Security Prices (CRSP) value-weighted portfolio exhibit significant seasonality. Their seasonal behavior is quite similar to that of the smallest quintile of New York Stock Exchange (NYSE) stocks and the CRSP equally weighted portfolio during March through October. While January is strong for the two latter portfolios, December, November, and January appear to be consistently strong for the two former portfolios. The seasonal pattern has, however, changed substantially over time. While June and July returns experienced a significant drop in seasonal strength, March and April returns gained seasonal strength for all four portfolios from 1927–1958 to 1959–1984. These changes coincide in an inverse fashion with the shifts in interest rate seasonality.     

Return Predictability of Turkish Stocks: An Empirical Investigation

Employing the portfolio method and cross-sectional regressions, this paper provides a comprehensive analysis of stock return predictability in Turkey from January 1997 to July 2011. In the risk-related predictors, we found predictive power for beta, total volatility, and idiosyncratic volatility. The "cheapness" variable, book-to-market ratio, is the most important return predictor for the stocks traded on the Istanbul Stock Exchange (now part of the Borsa Istanbul). Grouping the stocks as small and large according to the median value of the market capitalization of the stocks adds important insights to the analysis. Our results show the set of large stocks on the Istanbul Stock Exchange to be the least predictable set of stocks.     

THE CROSS-AUTOCORRELATION OF SIZE-BASED PORTFOLIO RETURNS IS NOT AN ARTIFACT OF PORTFOLIO AUTOCORRELATION

Prior studies find evidence of asymmetric size-based portfolio return cross-autocorrelations where lagged large firm returns lead current small firm returns. However, some studies question whether this economic relation is independent of the effect of portfolio return autocorrelation. We formally test for this independence using size-based portfolios of New York Stock Exchange and American Stock Exchange securities and, separately, portfolios of Nasdaq securities. Results from causality regressions indicate that, across all markets, lagged large firm returns predict current small firm returns, even after controlling for autocorrelation in small firm returns. These cross-autocorrelation patterns are stronger for Nasdaq securities.     

ON THE INFORMATIONAL EFFECT OF SHORT‐SALES CONSTRAINTS: EVIDENCE FROM THE TOKYO STOCK EXCHANGE

Using a database of stock lending fees for Japanese centralized margin transactions, I show that short‐sales constraints reduce the adjustment speed of stock prices to negative information before the announcements of revised earnings forecasts disclosed by firms in the Tokyo Stock Exchange from July 1998 to December 2001. I find that the cumulative abnormal returns (CARs) of the stocks with high short‐sales costs are insensitive to negative information on pre‐announcement days, but the CARs of these stocks become significantly lower than the CARs of the stocks with low short‐sales costs when the announcements reveal negative information to the public.     

Investor Sentiment and Stock Returns: Evidence from Turkey

This paper investigates the relation between investor sentiment and stock returns on the Istanbul Stock Exchange, employing vector autoregressive (VAR) analysis and Granger causality tests. The sample period extends from July 1997 to June 2005. In the VAR models, stock portfolio returns and investor sentiment proxies are used as endogenous variables. Two dummy variables accounting for natural and economic crises are used as exogenous variables. The analysis results suggest that, excepting shares of equity issues in aggregate issues, stock portfolio returns seem to affect all investor sentiment proxies, namely closed-end fund discount, mutual fund flows, odd-lot sales-to-purchases ratio, and repo holdings of mutual funds. Investor sentiment does not appear to forecast future stock returns; only the turnover ratio of the stock market seems to have forecasting potential.     

Tehran stock exchange prediction using sentiment analysis of online textual opinions

We investigate the impact of social media data in predicting the Tehran Stock Exchange variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about 3 months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon‐based and learning‐based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi‐regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in the Tehran Stock Exchange is different depending on their attributes. Moreover, we indicate that only comments volume could be useful for predicting the closing price, and both the volume and the sentiment of the comments could be useful for predicting the daily return. We demonstrate that users' trust coefficients have different behaviours toward the three stocks.     

AN EVALUATION OF THE PERFORMANCE OF PORTFOLIOS SELECTED FROM VALUE LINE RANK ONE STOCKS: 1976–1982

In this study, the performance of portfolios selected from among Value Line rank one stocks is compared with portfolios consisting of randomly selected New York Stock Exchange and American Stock Exchange stocks. Results indicate that before considering transactions costs, active traders who invest in Value Line rank one stocks can earn positive excess returns. However, after considering transaction costs, neither active traders nor passive investors in rank one stocks can earn returns that are statistically greater than returns achieved by portfolios of randomly selected stocks. These results are not sensitive to variations in portfolio size.     

Multiobjective portfolio optimization with non-convex policy constraints: Evidence from the Eurostoxx 50

Our purpose in this paper is to depart from the intrinsic pathology of the typical mean–variance formalism, due to both the restriction of its assumptions and difficulty of implementation. We manage to co-assess a set of sophisticated real-world non-convex investment policy limitations, such as cardinality constraints, buy-in thresholds, transaction costs, particular normative rules, etc., within the frame of complex scenarios, which demand for simultaneous optimization of multiple investment objectives. In such a case, the portfolio selection process reflects a mixed-integer multiobjective portfolio optimization problem. On this basis, we meticulously develop all the corresponding modeling procedures and then solve the underlying problem by use of a new, fast and very effective algorithm. The value of the suggested framework is integrated with the introduction of two novel concepts in the field of multiobjective portfolio optimization, i.e. the security impact plane and the barycentric portfolio. The first represents a measure of each security's impact in the efficient surface of Pareto optimal portfolios. The second serves as the vehicle for implementing a balanced strategy of iterative portfolio tuning. Moreover, a couple of some very informative graphs provide thorough visualization of all empirical testing results. The validity of the attempt is verified through an illustrative application on the Eurostoxx 50. The results obtained are characterized as very encouraging, since a sufficient number of efficient or Pareto optimal portfolios produced by the model, appear to possess superior out-of-sample returns with respect to the underlying benchmark.     

A smooth non-parametric estimation framework for safety-first portfolio optimization

In this paper, we adopt a smooth non-parametric estimation to explore the safety-first portfolio optimization problem. We obtain a non-parametric estimation calculation formula for loss (truncated) probability using the kernel estimator of the portfolio returns’ cumulative distribution function, and embed it into two types of safety-first portfolio selection models. We numerically and empirically test our non-parametric method to demonstrate its accuracy and efficiency. Cross-validation results show that our non-parametric kernel estimation method outperforms the empirical distribution method. As an empirical application, we simulate optimal portfolios and display return-risk characteristics using China National Social Security Fund strategic stocks and Shanghai Stock Exchange 50 Index components.     

A predictive system integrating intrinsic mode functions,artificial neural networks,and genetic algorithms for forecasting S&P500 intra‐day data

There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.     

Collective mental accounting: an integrated behavioural portfolio selection model for multiple mental accounts

We propose a behavioural portfolio selection model called collective mental accounting (CMA), which integrates all mental sub-portfolios (mental accounts) in one mathematical model. Moreover, this study contributes to the literature of behavioural portfolio selection in three further ways: first, the CMA model can determine the proportions of wealth allocated to each mental sub-portfolio with and without input from the investor. Second, unlike other mental accounting models (MA), in CMA it is possible to define constraints on total asset holdings such as short-selling, and cardinality constraints. Third, in order to make CMA more tractable and mathematically elegant, we obtain a semi-definite programming representation of the model. We also present a numerical example to investigate the effects of short-selling constraints as well as to compare the portfolio recommendations, utility functions, feasibility, and optimality of the CMA and MA models. The results reveal that although both models’ solutions are mean-variance efficient, CMA outperforms MA in terms of behavioural efficient frontier and utility functions.     

Effects of Price of Gold on Bombay Stock Exchange Sectoral Indices: New Evidence for Portfolio Risk Management

Using daily data, this paper examines the relationship between the returns of gold and seven sectoral indices in the Bombay Stock Exchange (BSE) for the period from January 2000 to May 2018. Given the importance of gold in India, there are significant issues in a portfolio selection in that country. By addressing the hedged robust portfolio problems, this paper focuses on three vanilla portfolio problems: the maximum return portfolio allocation, the global minimum variance portfolio problem, and the Markowitz portfolio allocation by using various multiple generalized autoregressive conditional heteroskedasticity (GARCH) models. The paper finds that gold returns are significantly independent of the returns of the BSE sectoral indices. Besides, gold returns can help predict the future returns of the Consumer Durables and the Fast-Moving Consumer Goods indices as well as the Oil & Gas equity indices. Finally, the findings also show that gold hedges against the information technology stock index and serves as a robust portfolio diversification tool. With these new results, this paper offers several implications for investors and risk management purposes.     

Tick size,market structure,and market quality

Large tick sizes imposed on high-priced stocks on the Korea Stock Exchange (KSE) are significant binding constraints on bid-ask spreads. Nearly 60% of quoted spreads are equal to the tick size for stocks with the largest tick size and more than 87% of quoted spreads are equal to the tick size for stocks in the largest size portfolio. We also show that the average spread of KSE stocks with large tick sizes is greater than that of matched NYSE stocks, whereas the average spread of KSE stocks with the smallest tick size is smaller than the corresponding figure for the matched NYSE stocks. We interpret these results as evidence that traders on the KSE are paying large trading costs because of the artificially imposed large tick sizes.     

TRADING OF NASDAQ STOCKS ON THE CHICAGO STOCK EXCHANGE

We use a linear programming model to form two portfolios with approximately equal levels of attributes such as financial leverage. One portfolio comprises stocks that trade exclusively on NASDAQ and the other, stocks that trade on both the Chicago Stock Exchange (CSE) and NASDAQ (CSE/NASDAQ). We find that spreads are lower for the CSE/NASDAQ portfolio, but so is the percentage of quotes at spreads of $0.125. In fact, the lower spreads observed for the CSE/NASDAQ portfolio arise from fewer quotes with spreads of more than $0.25.     

Creation of an Islamic stock index in West Africa

Islamic finance, which has become inescapable has entered into financial markets by offering solutions consistent with the precepts of the Shariah. The objective of this paper is to propose the creation of an Islamic stock index in West Africa. The choice of market values ​​requires extra financial and financial screening system. The results show that 10% of the securities in Nigeria Stock Exchange , the Regional Stock Exchange (Brvm) and the Ghana Stock Exchange (Gse) were selected to form the Islamic index " Ecowas Shariah index.     

Comparative issues between linear and non-linear risk measures for non-convex portfolio optimization: evidence from the S&P 500

Abstract

This article focuses on inferring critical comparative conclusions as far as the application of both linear and non-linear risk measures in non-convex portfolio optimization problems. We seek to co-assess a set of sophisticated real-world non-convex investment policy limitations, such as cardinality constraints, buy-in thresholds, transaction costs, particular normative rules, etc. within the frame of four popular portfolio selection cases: (a) the mean-variance model, (b) the mean-semi variance model, (c) the mean-MAD (mean-absolute deviation) model and (d) the mean-semi MAD model. In such circumstances, the portfolio selection process reflects to a mixed-integer bi-objective (or in general multiobjective) mathematical programme. We precisely develop all corresponding modelling procedures and then solve the underlying problem by use of a novel generalized algorithm, which was exclusively introduced to cope with the above-mentioned singularities. The validity of the attempt is verified through empirical testing on the S&P 500 universe of securities. The technical conclusions obtained not only confirm certain findings of the particular limited existing theory but also shed light on computational issues and running times. Moreover, the results derived are characterized as encouraging enough, since a sufficient number of efficient or Pareto optimal portfolios produced by the models appear to possess superior out-of-sample returns with respect to the benchmark.     

The Effect of Crossing‐Network Trading on Dealer Market's Bid‐Ask Spreads

This article provides new insights into market competition between traditional exchanges and alternative trading systems in Europe. It investigates the relationship between the trading activity of a crossing network (CN) and the liquidity of a traditional dealer market (DM) by comparing data from the SEAQ quote‐driven segment of the London Stock Exchange (LSE) and internal data from the POSIT crossing network. A cross‐sectional analysis of bid‐ask spreads shows that DM spreads are negatively related to CN executions. Risk‐sharing benefits from CN trading dominate fragmentation and cream‐skimming costs. Further, risk‐sharing gains are found to be related to dealer trading in the CN.