Buy Strategy Research Articles

KOMPARASI STRATEGI INVESTASI AKTIF DAN PASIF UNTUK OPTIMALKAN RETURN SAHAM YANG TERDAFTAR DI BURSA EFEK INDNESIA

The purpose of this study was to test an active strategy (moving leverage) with a passive strategy (buy, sell and hold strategy) to determine which strategy to generate optimal returns with to compair of all strategy. Method by collecting data is then processed and analyzed using software ChartNexus. An indicators moving everage 5, 10, 30, bollingerband and compare the return of an active strategy with passive strategies. The results of this research note that suitable and appropriate technical analysis to forecast stock prices to determine buy, sell and hold, to optimize stock returns and shows that an active strategy is able to out perform a passive strategy when the market is bearish and volatile. The use of passive strategies can provide superior results, when used on a very bullish market conditions. Volatile market conditions that are more suitable to use the strategy of comparison between the strategies of active and passive strategies.

A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology

Stock price forecasting is a classic problem facing analysts. Forecasting models have been developed for predicting individual stocks and stock indices around the world and in numerous industries. According to a literature review, these models have yet to be applied to the restaurant industry. Strategies for forecasting typically include fundamental and technical variables. In this research, fundamental and technical inputs were combined into an artificial neural network (ANN) stock prediction model for the restaurant industry. Models were designed to forecast 1 week, 4 weeks, and 13 weeks into the future. The model performed better than the benchmark methods, which included, an analyst prediction, multiple linear regression, trading, and Buy and Hold trading strategies. The prediction accuracy of the ANN methodology presented reached accuracy performance measures as high as 60%. The model also shown resiliency over the housing crisis in 2008.

Gold-Silver Ratio and Its Utilisation in Long Term Silver Investing

Gold-silver ratio is one of the best known ratios of commodity markets. The historical gold-silver ratio used to be approximately 15 which is almost in line with the estimates of geologists that there is 19 times more silver than gold in the Earth’s crust. The gold-silver ratio used to be administratively set during the era of bimetallism. But after silver and later also gold were eliminated from the monetary system, the gold-silver ratio started to fluctuate wildly. The gold-silver ratio is closely followed by many financial analysts and investors who try to estimate the future price development of both of the metals. The aim of this article is to find out whether the gold-silver ratio may be used as a cornerstone for a profitable long term investment strategy. The research shows that when investing in silver, a simple strategy based on the gold-silver ratio brings higher returns than the buy & hold strategy. The difference between returns of the two strategies is statistically significant in the long run. DOI: 10.5901/mjss.2016.v7n1p285

Why There Is No Inherent Equity Premium: The Total Market Return vs. the Per Share Return

The Equity (return) Premium is shown to emerge fully, specifically and episodically from just three transient factors: EPS growth above long term GDP/capita growth; after tax long bond yield below GDP/capita growth, and change in P/E (valuation) - without a risk premium. The earnings/price (E/P) ratio is shown to lack an embedded premium and appears anchored to GDP per capita growth. An aggregate stock market after-tax return model is proposed that balances return with GDP growth; which existing models do not. Long term investors (IRA, 401k, endowments, buy and hold strategies and pension plans) have no historical negative return risk over their investment-divestment horizon, enjoy the lowest effective tax rate, are the largest equity holder block with common economic attributes, and may therefore determine valuation by being the highest sustainable bidders.

Does Using Firefly Algorithm to Find Effective Optimized Trading Rules Produce Greater Results Compared to a Simple B&H Strategy?

The objective of this document is to find out if the Firefly Algorithm (FA), a nature-inspired metaheuristic optimization algorithm, can be used to find effective optimized trading rules and compare the results to a simple Buy and Hold strategy. FA is one of the most recent evolutionary computing models and was studied by several researchers who concluded that it is an optimal and powerful algorithm to resolve complex problems. Despite the fact that some algorithms like Genetic Algorithm or Particle Swarm Optimization proved in the last decades to be optimal metaheuristics to find the fittest parameter for a trading rule, it was found out with limits and gaps such as the frequent transaction costs. This paper is to show if FA can be used to find optimal technical trading rules and see if it can resolve the limits of the other algorithms. We use the FA to find out effective optimized trading rules and use them firstly for the testing phase with daily prices of the S&P500 index during the last recession period and compare it with an upward period for our training phase to be able to prove the effectiveness of the algorithm. After that, we compare it with a benchmark used in most litteratures, the Buy and Hold (B&H) strategy. After transaction costs, FA produced higher results compared to the B&H strategy during the training period. However, during the out-of-sample tests, it showed contrary results. On one side, FA gained a simulated out-of- sample profit of over 4% for the chosen periods when daily transaction costs were taken into account. On the other side, FA could not outperform the benchmark when monthly transactions were taken into account. This is probably due to the selected and predefined trading system. However, globally FA demonstrated to be a more efficient metaheuristic algorithm than the PSO.

Rebalancing versus Buy and Hold: Theory, Simulation and Empirical Analysis

We consider returns from rebalanced and buy and hold portfolios consisting of the same stocks. Theoretical properties are derived using Jensen's inequality and the Holder's Defect Formula. Simulations are used to confirm theory and to investigate ambiguous cases where theory is silent. Rebalancing generally increases the Sharpe Ratios and decreases total return volatility. Buy and hold produces greater expected return. Results are more opaque with respect to expected geometric means. Our empirical tests are based on portfolios composed of the risk-free asset and market valued weighted CRSP total returns. While rebalancing reduces volatility, these tests largely favor the buy and hold strategy due to the high relative returns of the index vis-a-vis the risk-free asset.

Trading System based on the use of technical analysis: A computational experiment

Previous studies highlight the influence of methods of technical analysis in the search for exceptional gains in the context of the financial market. Based on this scenario, the main objective of this paper is to analyze the performance of the Simple Moving Average, Exponential Moving Average, MACD and Triple Screen techniques in an actual trading system that included 198 stocks traded in the Brazilian. This paper studies the power of predictability of such methods using various combinations of periods, brokerage fees and a policy of Stop-Loss and compares these with the buy-and-hold strategy. The results indicate that while the studied techniques lead to a high probability of obtaining a return that exceeds the investment value, they have little power of predictability in the Brazilian market. In relation to the passive buy strategy, only the smallest part of the obtained returns outweighs the results of the buy-and-hold strategy.

"Prior Innovation Experience, In-House Design, and the Make-Buy Choice"

This study suggests two distinctive factors that can enhance the performance of both the make and buy strategies – prior architectural innovation experience (PAIE) and the designing of outsourced c...

The Joint Cross Section of Options and Bonds

This paper examines the cross section of options implied volatility and corporate bond returns. We document a strong predictive ability of corporate bond returns using changes in call and put options implied volatility. Specifically, a strategy of buying (selling) the portfolio with lowest (highest) changes in options implied volatility yields an average monthly bond return of 1.03% in excess of the risk free rate. Returns based on this strategy are statistically highly significant and economically very meaningful. The predictive ability persists up to two months. In contrast, we find no evidence that bond prices incorporate information prior to option and stock prices. Since bond investors are generally sophisticated institutional investors who process information in an efficient manner and the predictive ability of options is relatively long, we conclude that informed trading rather than superior information processing abilities is responsible for the predictive ability of options.

The Information Content of Earnings Forecasts and a Portfolio Application

This study develops a framework to compare the ability of alternative earnings forecast approaches to capture the market expectation of future earnings. Given prior evidence of analysts’ systematic optimistic bias, we decompose earnings surprises into analysts’ earnings surprises and adjustments based on alternative forecasting models. An equal market response to these two components indicates that the associated earnings forecast is a sufficient estimate of the market expectation of future earnings. To apply our framework, we examine four recent regression-based earnings forecasting models, alongside a simple earnings-based random walk model and analysts’ forecasts. Using the earnings forecasts of the model that satisfies our sufficiency condition, we identify a set of stocks for which the market is unduly pessimistic about future earnings. The investment strategy of buying and holding these stocks generates statistically significant abnormal returns. We offer an explanation as to why this and similar strategies might be successful.

EMERGENT CAPITAL MARKETS’ EFFICIENCY: THE CASE OF POLAND

We apply two well-known technical indicators to the Polish Stock index over the period of 9/4/1998 to 4/18/2013. Our findings support the predictive power of technical trading rules for each sub-period and for the entire period. We then ask whether an investor can use the predictive power of technical analysis to beat the profitability of the buy-and-hold strategy considering both transaction costs and risk. We conclude that it is not possible to beat the buy and hold strategy when considering transaction costs and risk. We could say the Polish market is efficient.

Comparing Lifecycle Sustainment Strategies in an Electronic Component Obsolescence Environment

Rapid advancements in technology and the diminishing lifecycle of electronic systems have complicated the sourcing and sustainment activities of many organizations as suppliers of original components go out of business or refuse to produce obsolete products. This article explores diminishing manufacturing sources and material shortages as well as the obsolescence costs and reliability issues associated with electronic components. Using the current United States Air Force situation as an example, the overall research question asks how obsolescence management can be improved through various sourcing strategies. This article utilizes a simulation model to evaluate equipment demand requirements and sustainment costs for three different approaches: (1) a re-engineering strategy, (2) a lifetime buy strategy, and (3) a programmed redesign strategy. Statistical analysis and long-term forecasted cost comparisons of these three strategies provide a framework to help acquisition, and sustainment managers determine t...

Is the ‘buying winners and selling losers’ trading strategy profitable in the New Economy?

Using recent US financial market data, this study tested whether relative strength trading strategy was profitable in two different sample periods (1990 to 2012 and 1965 to 2012). In contrast to the previous findings, our study finds no clear evidence for profitable zero-cost buy and hold strategy for 3- to 12-month periods for the period 1990 to 2012. However, we find few profitable zero-cost strategy for the period 1965 to 2012, but the returns are much smaller than previously reported. These findings may imply gain in market efficiency in the US financial markets in recent period.

An Intermarket Approach to Beta Rotation: The Strategy, Signal, and Power of Utilities

It is often said by proponents of the Efficient Market Hypothesis that no strategy can consistently outperform a simple buy and hold investment in broad stock averages over time. However, using a strategy based on the principles of intermarket analysis, we find that this assertion is not entirely accurate. The Utilities sector has many unique characteristics relative to other sectors of the broader stock market, including its higher yield, lower beta, and relative insensitivity to cyclical behavior. Our analysis suggests that rolling outperformance in the sector is not only exploitable, but also provides important signals about market volatility, seasonality, and extreme market movement. We explore historical price behavior and create a simple buy and rotate strategy that is continuously exposed to equities, positioning into either the broad market or the Utilities sector based on lead-lag dynamics. Absolute performance and risk-adjusted returns for this beta rotation approach significantly outperform a buy and hold strategy of the market and of the Utilities sector throughout multiple market cycles.

Inter-Temporal Risk Parity: A Constant Volatility Framework for Equities and Other Asset Classes

Inter-temporal risk parity is a strategy which rebalances between a risky asset and cash in order to target a constant level of risk over time. When applied to equities and compared to a buy and hold strategy it is known to improve the Sharpe ratio and reduce drawdowns. We used Monte Carlo simulations based on a number of time series parametric models from the GARCH family in order to analyze the relative importance of a number of effects in explaining those benefits. We found that volatility clustering with constant returns and the fat tails are the two effects with the largest explanatory power. The results are even stronger if there is a negative relationship between return and volatility. On the other hand, if the Sharpe ratio remains constant over time, the only benefit would arise from an inter-temporal risk diversification effect which is small and has a negligible contribution. Using historical data, we also simulated what would have been the performance of the strategy when applied to equities, corporate bonds, government bonds and commodities. We found that the benefits of the strategy are more important for equities and high yield corporate bonds, which show the strongest volatility clustering and fat tails. For government bonds and investment grade bonds, which show little volatility clustering, the benefits of the strategy have been less important.

The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P 500

The aim of this research is to analyse the effectiveness of the Chicago Board Options Exchange Market Volatility Index (VIX) when used with Support Vector Machines (SVMs) in order to forecast the weekly change in the S&P 500 index. The data provided cover the period between 3 January 2000 and 30 December 2011. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as Relative Strength Index, Moving Average Convergence Divergence, VIX and the daily return of the S&P 500. The SVM identifies the best situations in which to buy or sell in the market. The two outputs of the SVM are the movement of the market and the degree of set membership. The obtained results show that SVM using VIX produce better results than the Buy and Hold strategy or SVM without VIX. The influence of VIX in the trading system is particularly significant when bearish periods appear. Moreover, the SVM allows the reduction in the Maximum Drawdown and the annualised standard deviation.

Search for Alpha-Ema Weighted Momentum Based Approach

By exploiting momentum strategy; “buying winner portfolio and selling portfolio” this research papers investigates the evidence against the strong form of efficiency which claims that one cannot earn excess return with historical information and excess return if any, is mere compensation of excess risk taking behavior. The study undergone during the comprehensive research reveals the evidences of the weak form of efficiency and presence of meager weak momentum effect with holding period 1 month and in rest of the cases, on inclusion of transaction cost, such strategy will perform worse than buy and hold strategy. The trading strategy has been tested on SP and to De Bondt and Thaler’s contrarian effect. In addition, the author in his research has tried to analyze the causes of momentum and contrarian effects in general and results are analyzed from the perspective of behavioral finance. Also, the paper unfolds the comprehensive analysis of the calendar effect in the markets that might contribute to the weak momentum effect observed in the result.

Make, buy or both? R&D strategy selection

Abstract The aim of this paper is to increase our knowledge of a firm's innovative behaviour by jointly analysing its internal resources, industry characteristics and appropriability conditions as drivers of its R&D strategy selection: make, buy and make-buy. Based on panel data (1992–2005) covering 1539 Spanish manufacturing firms, results show that firms lacking organisational resources and competing in stable markets prefer the buy strategy. Firms with a high level of technological resources that are immersed in high-tech industries are prone to selecting the make-buy strategy. Internationalised firms with high levels of appropriability prefer the make strategy.

Commodities as Inflation Protection

Inflation degrades the function of the U.S. dollar as a storehouse of value. The goods and services which constitute the cost of living become more expensive in dollar terms. Commodities, and gold in particular, are often recommended to hedge that risk. Our work casts that popular advice into doubt, showing that inflation hedging with commodities is difficult, risky, and ultimately unreliable. Buy and hold strategies are impractical for many, and inflation hedging with commodities requires bold and successful management, together perhaps with good luck. Examining fifty-three years of spot prices for forty-five commodities and fourteen commodity aggregate indices, we find that owning certain things – specific commodities – rather than dollars would have been a better storehouse of value in the inflationary 1970s and in the brief period of high inflation from 1989-90. However, various commodities performed very differently in different periods, and some of the best in one period proved to be the worst in another. A basket of commodities to protect against inflation would be difficult to construct and in result. Furthermore, the precious metals, while often cited as inflation hedges, carry significant timing risks. Finally, a diversified basket of energy commodities has performed well under historical inflation, but carries its own risks.

Allocation of Assets on the Ghana Stock Exchange (GSE)

In this paper, we use stock price data between the years 2007 and 2010 to investigate the allocation of assets on the GSE. The Classical Markowitz optimization method shows that, the most profitable portfolio is obtained by investing 90% of wealth in non-financial assets and 10% in financial assets. Risk aversive investor who goes for the minimum risk portfolio has to invest 80% in non-financial assets and 20% in financial assets. We also find that, if the investor decides to split his wealth among the financial and non-financial asset equally, his profit will not be as much as the minimum and the optimum risk portfolio. In effect, there is a reward for risk on the GSE but the Markowitz optimization strategy never exceeds the buy and hold strategy of the market index.