Simple Buy Research Articles

Understanding and Testing Iceberg Algorithmic Trading Strategy

As Technology has advanced in recent times, it also has impacted the Trading requirements that modern day traders have. Trading now is not a simple Buy and Sell of a Security, but it is way complex involving complex mathematical algorithms programmed to execute Trades at much faster rates and using different trading strategies, also called Algorithmic Trading strategies. Traders’ selection of algorithmic strategy/ order types is a critical decision that significantly influences the execution quality and outcome of trades. In this Paper, we will specifically study one of the commonly used Algorithmic strategy/ order type called Iceberg Order. We will try to understand why Iceberg Orders are required, how they are executed with the help of different examples and what Testing should be done to ensure we deploy a Defect free Iceberg Order Strategy to production. A small issue in execution of trading strategy can result in big losses to the company, regulatory fines and reputational loss. So, Test Scenarios to be considered and testing requirements for Iceberg Orders should consider different scenarios which will be discussed in detail in this paper.

Selling options to beat the market: Further empirical evidence

Brownian-motion-linked pricing models predict the existence of derivatives whose value at risk in short positions is lower than their price. The derivative sale plus the price investment in riskless assets becomes self-financing with negative risk. Repeating over and over this strategy, the price remains zero, but the risk tends to minus infinity. This paper reports results of empirical studies about the performance of this strategy. The American SP-500 and the German DAX-30 are involved. In both cases the index is beaten by simple buy and hold strategies containing riskless assets and short options. This finding may be interesting to practitioners and theoretically relevant. Firstly, the market can be beaten in an orthodox way, since results of Financial Economics inspire the methodology. Secondly, the risk never tends to minus infinity in practice. Perhaps the theoretical behavior in tails of Brownian-motion-linked models should be revisited.

주식시장국면 예측과 투자전략에 대한 연구

Purpose - This study investigates the performance of investment strategies incorporating estimated stock market cycle based on a lead-lag relationship between business cycle and stock market cycle, thereby deriving empirical implications on risk management.
 Design/methodology/approach - The data period ranges from June 1953 to September 2022 and de-trended short rate, term spread, credit spread, stock market volatility are considered as major input variables to estimate business cycle and stock market cycle by applying probit model. Based on the estimated stock market cycle, two types of strategies are constructed and their performance relative to the benchmark is empirically examined.
 Findings Two types of strategies based on stock market cycle are considered: The first strategy is to long(short) on stocks when stock market stage is expected to be an expansion(a recession), and the second one is to long on stocks(bonds) when expecting an expansion(a recession). The empirical results show that the strategies based on stock market cycle outperforms a simple buy and hold strategy in both in-sample and out-of-sample investigation. Also the out-of-sample evidence suggests that the second strategy which is in line with asset allocation is more profitable than the first one.
 Research implications or Originality The strategies considered in this study are based on the estimated stock market cycle which only depends on a few easily available financial variables, thereby making easier to establish such a strategy. It implies that investors enhance investment performance by constructing a relatively simple trading strategies if they set their position on stocks or choose which asset class to buy conditioning on stock market cycle.

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.

Implementation perspectives of solar energy irrigation policy in water deficient regions: A case of Rajasthan state in India

Groundwater extraction through subsidized solar power has flared up a number of issues in water resource planning in water deficient regions. Previous studies and research suggests ‘buy back solutions’ to check indiscriminate exploitation of ground water reserves; however, the present study is an attempt to put forward a theoretical incentive framework analysing the condition in Rajasthan state to justify why farmers would not opt to sell excess electricity generated to the grid rather choose to sell solar irrigation. The study validates the inappropriateness of simple buy back solution to check excessive groundwater extraction and concludes that one-way approach in policy implementation regarding groundwater extraction can be a misnomer and region-specific policies should be adopted looking to the water deficiency or abundance in the region.

Managing Trading Decisions Using The True Strength Index –An Applied Study

The purpose of this research is to test the ability of the true strength index To time and manage trading in the financial market to select the best stocks and achieve a higher return than the Simple buy and hold strategy. And To achieve the objectives of the research, it relied on the main hypothesis, which is By using the True Strength Index to manage trading decisions buying and selling, can be achieved higher returns than the buy and hold strategy . The research community has been identified with all stocks listed on the Iraq Stock Exchange. Implementing the financial research tests requires selecting a sample from the research community that fulfills the test requirements according to a number of conditions So (38) companies were nominated to be the research sample It was distributed by (17) companies in the banking sector. and (2) companies in the insurance sector, and (4) companies in the service sector, and (7) companies in the industrial sector, and (6) companies in the hotel and tourism sector, and one company in the telecommunications sector, And one company in the agricultural sector, This sample constituted approximately (30%) of the research community of (128) companies. The research period extended from (1/2/2016) to (31/12/2021), by Using financial and statistical methods, the research reached a number of conclusions, the most important of which are Trading can be timed and managed successfully using the True Strength Index and using it with General historical information can achieve returns that exceed the returns of buy and hold.

Does Volatility Harvesting Really Work?

A large number of academic papers claim that there is a consistent advantage to rebalancing an investment portfolio relative to a simple Buy&Hold strategy, while other academic papers claim the opposite. The papers on both sides are typically based on unclear mathematical arguments with only few real-world examples. This paper does not try to give a mathematical explanation but is entirely empirical. We use daily stock-returns for more than 900 U.S. stocks between the years 2007 and 2021, where we generate thousands of random portfolios of different sizes and start/end-dates, to compare the Rebalanced and Buy&Hold strategies on different performance metrics. Although the Rebalanced portfolios may seem to perform better than the Buy&Hold portfolios in terms of e.g. the arithmetic mean daily return, it actually depends on which period we consider. We also make preliminary experiments with intraday data, where it does appear that Rebalancing is consistently better than Buy&Hold, although these data-sets are so small that we cannot conclude this advantage holds in general.

Employing Machine Learning Algorithms to build Trading Strategies with higher than Risk-Free Returns

This research aims at exploring whether simple trading strategies developed using state-of-the-art Machine Learning (ML) algorithms can guarantee more than the risk-free rate of return or not. For this purpose, the direction of S&P 500 Index returns on every 6th day (SPYRETDIR6) and magnitude of S&P 500 Index daily returns (SPYMAG) were predicted on a broad selection of independent variables using various ML techniques. Using five consecutive data spans of equal length, GBM was found to provide highest prediction accuracy on SPYRETDIR6, consistently. In terms of magnitude prediction of daily returns (SPYMAG), Random Forest results indicated that there is a very high correlation between actual/predicted values of SPY. Based on these results, Trading Strategy #1 (using SPYRETDIR6 predictions) and Trading Strategy #2 (using SPYMAG predictions) were developed and tested against a simple Buy & Hold benchmark of the same index. It was found that Trading Strategy #1 provides negative returns on all data spans, while Trading Strategy #2 has positive returns on average when data is separated into consecutive data spans. None of the trading strategies have a positive Sharpe ratio on average, but Trading Strategy #2 is almost as profitable as investing in T-bills using the risk-free rate.

A Research on Trading of Sensex Stocks by using RSI

This study has the aim of doing advanced research on technical analysis by finding reliability of buy or sell signals generated by the Relative Strength Index tool. In this study, the RSI tool is tested on Sensex components (30 stocks) for the last six months. The data set for this study had been the daily data, taken from NSE and BSE website. The study compares the return from RSI recommendations and simple buy and hold strategy returns. The study also aims at testing the RSI tool for its probability of generating false buy sell signals. The findings of this study clearly indicate that RSI can be a powerful tool in the markets over buy and hold strategy. The study also makes it clear that the probabilities of false signals are also limited.

Empirical Analysis of VDAX and VSTOXX as Major Volatility Indices in the EU Including Forecasting Tools

This study reviews various time series forecasting models in order to find the best fit for the VDAX and VSTOXX for one month and one year. Additionally, the influence of the trading volume of the DAX is examined. Both durations are found to be stationary by the Phillips-Perron test, that is why non-integrated models are used. For a duration of one month, a GARCHX(1,1) model is the best fit in-sample as well as out-of-sample, while the best fit for a duration of one year is found to be a ARX(1) model. Based on the forecasts, two trading strategies are tested for each duration, which is a long only strategy and a combination of long and short trades. The performance of both strategies is compared with a simple buy and hold strategy on each VDAX and VSTOXX. It is found that an excess return over the buy and hold strategy can be generated for both durations even with transaction costs.

Estimating the impact of domain-specific news sentiment on financial assets

The influence of news on financial markets has been studied by extracting opinions and sentiment from text using content analysis and natural language processing techniques, and then using this measure to estimate the impact of sentiment on price changes. We present a method and implementation that analyses the content of news using multiple dictionaries that accounts for the specific use of terminology in a given domain. To evaluate our approach we build different collections of domain related news for two financial markets and examine the impact that topical news has on two financial benchmarks in the equity and oil markets. We examine how the level of news sentiment from different news sources influences financial returns over time. We create a trading signal based on the news impact that predicts next day returns in the Dow Jones Industrial Average and West Texas Intermediate crude oil. We find that incorporating news sentiment into a trading strategy increases annual returns over a simple buy and hold strategy for both markets.

Quiet Period Reit Returns

Executive SummaryExcess returns around the expiration of the IPO quiet period documented for industrial IPOs are minimal for REITs, supporting the argument that REITs are more transparent than other firms. The existence of analyst coverage impacts quiet period returns for REITs only during the pre-bubble period when coverage is less comprehensive. The frequency of analyst recommendations issued immediately after the quiet period for REITs is lower than for industrial IPOs, which again suggests greater REIT transparency since there is an implied lower need for coverage. Recommendations in number and in simple buy or sell categorization have a slight impact on returns. With marginal statistical significance, the small number of firms followed by four or more analysts posts excess returns while the very small number of firms with no buy recommendations posts negative excess returns.

Can Sentiment Indicators Signal Market Reversals?

In this study we use machine learning algorithm to test Amareos sentiment indicator's predictive power of market reversals. We then build and test a viable trading strategy.As input for the algorithm, we used eight market sentiment indicators (Anger, Anticipation, Disgust, Fear, Gloom, Joy, Optimism and Sentiment) on 20 major equity indices from January 1, 2005 to April 15, 2016.As the target output, we use a classification of the performance of the indices on the following 182 days - approximately six months - split between bottom, top and neutral days.Our learning algorithm is of the type called random forest. Through calibration on a training set composed of 64% of the data, we obtain a final set of decision trees, or forest. We then examine the out of sample accuracy of this forest on the remaining 36% of the data.As the accuracy on the test set is relatively high - a result that cannot be explained just by luck - we simulate a trading strategy based on the forest output. The resulting trading strategy produces strong performance, certainly much better than a simple buy and hold, even when adjusted for risk.

The Blended Approach to Real Estate Allocations: Performance Implications of Combining an Exposure to German Spezialfonds with Global Listed Real Estate Securities

This paper seeks to increase the understanding of the performance implications for investors who choose to combine an unlisted real estate portfolio (in this case German Spezialfonds) with a (global) listed real estate element. We call this a “blended” approach to real estate allocations. For the avoidance of doubt, in this paper we are dealing purely with real estate equity (listed and unlisted) allocations, and do not incorporate real estate debt (listed or unlisted) or direct property into the process. A previous paper (Moss and Farrelly 2014) showed the benefits of the blended approach as it applied to UK Defined Contribution Pension Schemes. The catalyst for this paper has been the recent attention focused on German pension fund allocations, which have a relatively low (real estate) equity content, and a high bond content. We have used the MSCI Spezialfonds Index as a proxy for domestic German institutional real estate allocations, and the EPRA Global Developed Index as a proxy for a global listed real estate allocation. We also examine whether a rules based trading strategy, in this case Trend Following, can improve the risk adjusted returns above those of a simple buy and hold strategy for our sample period 2004-2015. Our findings are that by blending a 30% global listed portfolio with a 70% allocation (as opposed to a typical 100% weighting) to Spezialfonds, the real estate allocation returns increase from 2.88% p.a. to 5.42% pa. Volatility increases, but only to 6.53%., but there is a noticeable impact on maximum drawdown which increases to 19.4%. By using a Trend Following strategy raw returns are improved from 2.88% to 6.94% p.a. , The Sharpe Ratio increases from 1.05 to 1.49 and the Maximum Drawdown ratio is now only 1.83% compared to 19.4% using a buy and hold strategy . Finally, adding this (9%) real estate allocation to a mixed asset portfolio allocation typical for German pension funds there is an improvement in both the raw return (from 7.66% to 8.28%) and the Sharpe Ratio (from 0.91 to 0.98).

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.

Measuring the Predictability of the Oil and Gas Stock Returns and the Performances of Moving Average Trading Rules

The paper re-examines whether investors can predict oil and gas stock prices for abnormal returns using autocorrelation-based trading and filter rules and moving average based strategies. Short and long lengths moving averages were employed and their performances measured against the returns from simple buy and hold investment strategy. The application of the trading rules employed has not shown the possibility that investors can make abnormal returns in oil and gas stocks. The performances of short and long moving averages examined have not shown any conclusive evidence that any of the moving averages results in more return than the other.

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.

Price Earning Ratio Effect : A Test of the Semi Strong Form of Efficient Market Hypothesis in Indian Stock Market

Proponents of Semi strong form of Efficient Market Hypothesis (EMH) claim that security prices fully reflect all publicly available information in a rapid and unbiased manner. Opponents of this Hypothesis question its validity by explaining various anomalies in stock markets. One such anomaly that they elucidate is Price Earning (P/E) ratio Effect, which is based on the premise that P/E ratios are indicators of the investment performance of a security and low P/E stocks have a tendency to outperform high P/E stocks even after adjusting for underlying risks.The purpose of the study is to empirically test the relationship between P/E ratios and equity returns in Indian stock market based on monthly stock returns of 90 companies during the period April 2006 – June 2012 and thereby to examine the validity of semi strong form of EMH. The study applies Jensen, Sharpe and Treynor measures, which are based on Sharpe-Linter Capital Asset Pricing Model (CAPM) to test the risk- return relationships of these portfolios and to compare whether portfolio of low P/E stocks outperforms the portfolio of High P/E stocks. The study attempts to test, if there is any statistically significant difference between the returns of such a portfolio and a simple buy and old strategy. The study also attempts to examine if there is any statistically significant difference between the returns of Lowest P/E portfolio and Highest P/E portfolio using an alternative specification of CAPM. The findings of the study explain the superior performance of low P/E portfolio to high P/E portfolio, indicating the premium associated with cheap stock.

Detection of Crashes and Rebounds in Major Equity Markets

Financial markets are well known for their dramatic dynamics and consequences that affect much of the world's population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framework to understand and diagnose financial bubbles from rational expectations and was recently extended to negative bubbles and rebounds. Using the JLS model, we develop an alarm index based on an advanced pattern recognition method with the aim of detecting bubbles and performing forecasts of market crashes and rebounds. Testing our methodology on 10 major global equity markets, we show quantitatively that our developed alarm performs much better than chance in forecasting market crashes and rebounds. We use the derived signal to develop elementary trading strategies that produce statistically better performances than a simple buy and hold strategy.

Detection of crashes and rebounds in major equity markets

Financial markets are well known for their dramatic dynamics and consequences that affect much of the world's population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framework to understand and diagnose financial bubbles from rational expectations and was recently extended to negative bubbles and rebounds. Using the JLS model, we develop an alarm index based on an advanced pattern recognition method with the aim of detecting bubbles and performing forecasts of market crashes and rebounds. Testing our methodology on 10 major global equity markets, we show quantitatively that our developed alarm performs much better than chance in forecasting market crashes and rebounds. We use the derived signal to develop elementary trading strategies that produce statistically better performances than a simple buy and hold strategy.