Buy Strategy Research Articles

The impact of a firm's make, pseudo-make, or buy strategy on product performance

The bulk of the product architecture and make-buy choice literature deals with product architecture changes from integral to modular form. This development is often associated with a firm's tendency to change from a make to a buy strategy. However, a few studies investigate the change of product architecture in the reverse direction – from modular to integral form – and the subsequent change in the firm sourcing decision from a buy to a make strategy. These studies hold to the presumption that a firm following a make strategy will outperform firms following a buy strategy in dealing with integral product architectures. Based on the knowledge-based view, we argue for the viability of a sourcing strategy between the pure make and buy strategies – a pseudo-make strategy. We also argue that as product architecture changes from a modular to integral form, firms adopting this pseudo-make strategy are likely to show better product performance than firms following a pure make or buy strategy due to the relative knowledge advantages of the pseudo-make strategy in dealing with the integral product architecture. We examine the impact of the make/pseudo-make/buy strategies on product performance in the U.S. bicycle derailleur and freewheel market from 1980 to 1992 and provide theoretical and managerial implications of our results. Our findings highlight an important distinction between the pseudo-make and make-buy strategies that has not previously been fully appreciated in the extant literature, and as a result increases our understanding of why some firms do not switch strategies from a buy to a make strategy when product architecture changes from modular to integral form as previously expected.

Is Buy and Hold Investing Really Dead?

The objective of this paper is to create a metric, called the MT-BH indicator (Market timing- Buy and Hold) to determine when conditions are favorable for the opposing investing strategies of buy and hold or market timing. Macro-economic variables of GDP growth rate and inflation growth rate are then regressed to explain the favorability of buy and hold or market timing investing strategies. Using MSCI returns from 44 countries for the period of 1995 thru 2008, I found that high levels of inflation rate and GDP growth rate were both significant and favored a buy and hold investing strategy. Conversely, a market timing strategy was favorable when GDP and inflation rates were low or negative. These results were robust to country level of development and negative market return years within each country. The MT-BH indicator gives investors and brokers a metric which provides a signal when to adjust investing strategies from a buy and hold strategy to a market timing strategy.

Should Individual Investors Use Technical Trading Rules to Attempt to Beat the Market?

Problem statement: Despite widespread academic acceptance of the Effic ient Markets Hypothesis, some stock traders still use technical trading rules in an attempt to beat the market. Approach: This study looked at four trading rules, namely, t he arithmetic moving average, the relative strength index, a stochastic oscillator an d its moving average. These trading rules compare the relationship of current prices to past price pa tterns to generate a signal when to buy and sell stocks. The trading rules were tested over the year s 2000-2009, a period of time that exhibited bull and bear markets, to determine if traders could act ively trade a stock and beat a passive investment strategy. Results: We tested the four trading rules against the 576 s tocks that comprise the S&P 100, the NASDAQ 100 and the S&P Midcap 400. The results proved discouraging to that strategy, in that no one trading rule consistently beat the market. Conclusion/Recommendations: Since technical trading rules cannot be used to consistently beat a long-te rm buy and hold strategy, we recommend that investors first use fundamental analysis to select stocks and then apply a technical trading rule to enhance potential trading gains.

Previsão não-linear de retornos na BOVESPA: volume negociado em um modelo auto-regressivo de transição suave

Neste artigo, avalia-se a capacidade preditiva de um modelo auto-regressivo de transição logística suave (lstar) na geração de retornos estatisticamente significativos, quando se utiliza como variável de transição o volume negociado e o próprio retorno defasado, para o índice Ibovespa da Bolsa de Valores de São Paulo, analisado em termos diários entre os anos de 1996 e 2006. A justificativa para a inclusão do volume encontra-se em características do mercado e em algumas evidências de finanças comportamentais, que indicam que existe uma relação negativa entre volume e retornos futuros. O modelo possui, em geral, bom ajuste aos dados, embora não tenha a capacidade de gerar lucros reais adicionais, se os custos de transação são de 0.5% por operação. Para custos menores existe certa capacidade de previsão, embora inferior à de um modelo ar(1) e da estratégia de comprar e manter. considerando o risco, para custos de 0.035% por operação, o modelo auto-regressivo possibilita a obtenção de um índice de sharpe 20% maior do que com a estratégia de comprar e manter.

Predicting German Stock and Bond Returns with Macro-Variables: Evidence of Market Timing

We investigate the predictive ability of financial and macroeconomic variables for German stock and bond returns using a battery of performance metrics in addition to measures of superior predictive accuracy to identify the ‘best’ models. We also examine whether combination forecasts provide still better performance. The market timing ability of the best models is compared to simpler models and a naive buy and hold strategy. Changes in oil prices, industrial production growth and changes in short term interest rates are key predicators for stock returns. Additionally, for bond returns, exchanges rates and inflation play a role. We uncover clear evidence of the market timing ability of trading strategies based upon our best predictive models.

Genetic programming application to generate technical trading rules in stock markets

Technical trading rules can be generated from historical data for decision making in stock trading. In this study, genetic programming (GP) as an evolutionary algorithm has been applied to automatically generate such technical trading rules on individual stocks. In order to obtain more realistic trading rules, we have included transaction costs, dividends and splits in our GP model. Our model has been applied for nine Iranian companies listed on different activity sectors of Tehran Stock Exchange (TSE). Our results show that this model could generate profitable trading rules in comparison with buy and hold strategy for companies having frequent trading in the market. Also, the effect of the above mentioned parameters on trading rule's profitability are evaluated using three separate models.

Using relative movement to support ANN-based stock forecasting in Thai stock market

Over the years, Artificial Neural Networks (ANNs) have become a popular and seemingly accurate model to forecast stock prices. This paper proposes data preprocessing using relative movement to improve performance of ANN-based stock forecasting. Both fundamental and technical indicators are chosen as inputs to the system. The evaluation metrics include hit ratio and total return. The k-fold cross validation is utilised on a dataset of stocks in the banking sector in the Stock Exchange of Thailand (SET). The experiments show that the proposed model outperforms a traditional model, a random walk model, and a buy & hold strategy for both hit ratio and total return.

Stochastic integrals driven by fractional Brownian motion and arbitrage: a tale of two integrals

Recent research suggests that fractional Brownian motion can be used to model the long-range dependence structure of the stock market. Fractional Brownian motion is not a semi-martingale and arbitrage opportunities do exist, however. Hu and Øksendal [Infin. Dimens. Anal., Quant. Probab. Relat. Top., 2003, 6, 1–32] and Elliott and van der Hoek [Math. Finan., 2003, 13, 301–330] propose the use of the white noise calculus approach to circumvent this difficulty. Under such a setting, they argue that arbitrage does not exist in the fractional market. To unravel this discrepancy, we examine the definition of self-financing strategies used by these authors. By refining their definitions, a new notion of continuously rebalanced self-financing strategies, which is compatible with simple buy and hold strategies, is given. Under this definition, arbitrage opportunities do exist in fractional markets.

Market timing with aggregate accruals

We propose a market-timing strategy that aims to exploit aggregate accruals' return forecasting power. Using several performance measures of the aggregate accruals-based market-timing strategy, such as excess portfolio return, Sharpe ratio, and Jensen's alpha, we find robust evidence that, relative to the passive investment strategy of buying and holding the stock market, the market-timing strategy delivers superior performance that is both statistically and economically significant. Specifically, on average, the market-timing strategy beats the S&P500 index by 6 to 22 percentage points (annualized) after controlling for transaction costs over the 1980–2004 period.

Stochastic Optimization Methods for Buying-Low-and-Selling-High Strategies

This article is concerned with a numerical method using stochastic approximation approach for an optimal trading (buy and sell) strategy. The underlying asset price is governed by a mean-reverting stochastic process. The objective is to buy and sell the asset so as to maximize an overall expected return. One of the advantages of our approach is that the underlying asset is model free. Only mean reversion is required. Slippage cost is imposed on each transaction. Convergence of the algorithms is provided. Numerical examples are reported to demonstrate the results.

Determinants of Disposition and Reverse Disposition Effects

We develop a new measure for disposition effects based on the average length of a trading strategy and whether it is loss-making or profitable. Using this new measure, we are able to evaluate disposition effects in more detail than has been possible previously. We find for the first time the existence of disposition and reverse disposition effects. The reverse disposition effects are particularly prominent during falling markets, which is consistent with the prediction of Barberis and Xiong's (2008) model. Finally, the behavioral bias reduces for more experienced traders, traders holding investments for a longer period, and traders using buy strategies.

Testing Generic Rebalancing Policies for Retirement Portfolios

In this paper, I first study the out-of-sample performance of generic rebalancing policies selected using Monte Carlo, bagging and in-sample back-testing methods. The results show that rebalancing policies selected using simulation methods are more likely to perform better out-of-sample because estimation uncertainty is considered. Then rebalancing policy recommendations are made based on simulations using thirty years of data for three portfolios with different equity exposures and different market regimes. Portfolios managed with effective rebalancing policies outperform those managed with a buy and hold strategy in many situations. Among four kinds of rebalancing triggers (calendar, percentage-of-portfolio, fixed bands and equal probability), the percentage-of-portfolio triggers perform best for most portfolios and performance measures. The recommended rebalancing policies suggest that investors should monitor their portfolios on a monthly or quarterly basis, even though in the empirical distributions the actual rebalances following these policies do not occur frequently.

Hedge Fund Indexes Replica

Hedge Funds are private, lightly regulated pooled investments which have the aim of obtaining absolute returns. The name hedge fund (HF from here on) is misleading since the word hedge implies defensive management or insurance against bad times. We can say that there is a common misperception through which one assumes HFs have a market neutral position. This comes from the fact that the original idea of market neutral Hedge Funds from Alfred Winslow Jones (aim at zero exposure to specific equity market factors) has now developed in a larger and impressively growing investment sector. Freedom in the managing process and lack of regulation are probably the main reasons for the heterogeneity and variety of HFs. Another peculiarity of Hedge Funds is that the Fund Manager usually puts his own money at risk participating to the fund with a substantial part of his liquidity; this behaviour is called eating one's own cookies. The private nature and the lack of regulation takes us to the main limit in academic works on HFs: the limited information often plagued with some biases. In this paper a broad and well known set of indexes, the CSFB Tremont Hedge Fund is taken in exam. I chose this one because of its long history and from an academic point of view it seems to be less biased compared to other non-investable indices. This Index is sold outside the USA and has an investable version with a participation of $1,000, clearly with a target also on retail investors. The first step is the description of the main univariate statistics of HFs, from which the drawbacks and advantages encountered in HF investing are highlighted and in particular: non normality (e.g. the so-called negative skewed trading strategies) and autocorrelation (liquidity risk and data smoothing). In order to get rid of autocorrelation a filtration is performed. I apply a four moment CAPM to get the statistical properties of HFs over the simple mean variance framework in order to understand where their abnormal return comes from and to give a better idea about how much their value is when added to a general equity portfolio.The second part is going to provide a brief State of the Art in the Hedge Fund Evaluation Research. I apply the Style Analysis to replicate the CSFB Tremont Hedge Fund Index by setting up reasonable common location factors and putting a gearing factor by variance targeting. Before doing the linear replica I check for multicollinearity and statistical significance in the location factors adopting the approximated t-statistic proposed in Lobosco and DiBartolomeo (1997). The model is as general as possible intended to avoid non-linearity (as in Jaganathan et al 1994). The location factors are a wide representation of the alternative risk factors hidden in the CSFB Tremont. In the end of this first replication approach I take the first 6 years of returns and perform 12 months out of sample linear replica. Statistic of the linear replica are given and compared to Index. To justify this passive buy and hold strategy I give CUSUM Test results in order to check if there is stability in the weights of the model. Finally the out of sample statistics for each strategy are given and compared with the CSFB/Tremont Indexes.

Testing Methods and the Rebalancing Policies for Retirement Portfolios

Portfolio rebalancing policy is one of the most common policies given by financial advisors to their clients for managing their clients' retirement portfolios. The out-of-sample performances of different policy testing methods have rarely been compared. We propose using bootstrap aggregation (bagging) based on stationary bootstrap as an innovative way in testing rebalancing policies. We show that in most cases, by a three to one chance, the out-of-sample performances of the rebalancing policies chosen by bootstrap aggregation can be better than the ones chosen by Monte Carlo simulation or in-sample testing for several performance measures and three different portfolios. We use all three methods to select rebalancing policies from periodical rebalancing, the rebalancing policies with fixed bands triggers and equal probability triggers at different checking frequency and the buy and hold strategy. We find that the investors should check their portfolios more than once per year and fixed bands triggers are preferred to the equal probability triggers in most cases.

The Performance of the 130/30 Strategy in the Australian Equities Market

This paper empirically tests two 130/30 strategies using the top 50 stocks listed on the Australian Stock Exchange: a momentum returns driven 130/30 strategy, and a fundamental value based 130/30 strategy. The performance of these enhanced active strategies is evaluated, against traditional buy and hold strategies used by long-only managers as well as naive investments in market indices, over an eight year period with quarterly rebalancing. The results of back-testing and out of sample simulations reveal that momentum driven 130/30 strategies can earn significant alpha over a long investment horizon. The 130/30 portfolios also have higher information ratios and earn higher risk-adjusted returns compared to their long only counterparts.

Buying and Selling Stocks of Multi Brands Using Genetic Network Programming with Control Nodes

A new evolutionary method named “Genetic Network Programming with control nodes, GNPcn” has been applied to determine the timing of buying or selling stocks. GNPcn represents its solutions as directed graph structures which has some useful features inherently. For example, GNPcn has an implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so very compact graph structures can be made. GNPcn can determine the strategy of buying and selling stocks of multi issues. The effectiveness of the proposed method is confirmed by simulations.

Eurodollar Exchange Rate Sensitivity of Dutch AEX Listed Firms

Eurodollar Exchange Rate Sensitivity of Dutch AEX Listed Firms

Evaluating the Seasonality and Persistence of ETFs Performance and Volatility: Implications for Profitable Investing

By breaking down ETFs in broad, sector and international market categories as well as in large, medium and small cap classes of capitalization we demonstrate that a substantially positive November effect influences the performance of all ETF market categories and classes of capitalization. We also reveal the existence of a strong reverse December effect along with a modest reverse effect that affect the ETFs' volatility. The modest November effect in risk is accompanied by a strong reverse November effect in tracking error related to all ETF categories and classes. Moreover, we provide slight evidence for persistence in ETFs' November performance and strong evidence for persistence in ETFs' November risk and tracking error. Further research indicates that the investing strategies which respect the November patterns in ETFs' performance can beat the buy and hold strategies on an average and accumulated level during a five-year period and that investors can gain significant returns if they allow themselves to be exposed in greater level of volatility.

Tests of Technical Analysis in India

The study evaluates the economic feasibility of technical analysis in the Indian stock market. It discusses that technical indicators do not outperform Simple Buy and Hold strategy on net return basis for individual stocks. Technical indicators seem to do better during market upturns compared to market downturns. However, technical based trading strategies are not feasible vis-à-vis passive strategy irrespective of market cycle conditions. Technical indicators also do not provide economically significant profit for industry as well as economy based data. Combining fundamentals with technical information, we find, that technical indicators are more profitable for small stocks compared to big stocks and for high value stocks compared to low value stocks. However, the economic feasibility of fundamentals' based technical strategies is still questionable. Our results seem to confirm with the efficient market hypothesis.

A neurofuzzy model for stock market trading

This study investigates the forecasting ability of trading strategies based on neurofuzzy models, recurrent neural networks and linear regression models. The performance of the trading strategies was considered upon the prediction of the direction-of-change of the market in case of Nikkei 255 Index returns. The results demonstrate that the profitability of the trading rule based on the neurofuzzy model is consistently higher to that of the other models as well as of a buy and hold strategy during bear market periods.