While prior equity performance research analyzes portfolio characteristics using multifactor models, portfolio groups are typically used to explain average returns. Instead, we explore annual firm-level data and compare this with annual percentage changes in firm characteristics, emphasizing model predictive power and individual variation. Our analyses show a significant link between individual firm equity returns and percentage changes in total assets, book-to-market ratios, current ratios and shares outstanding, as well as historical returns and average market returns. Our findings affirm prior work illustrating the importance of profitability, size, liquidity, momentum and market returns, although we observe minimal evidence of the importance of investment in capital expenditures. We also perform these analyses at the industry level and note differences across industries, including the cyclical nature of the business equipment and consumer durables industries in contrast to the utilities and energy sectors. Overall, we contribute to the understanding of corporate characteristics and equity performance.
{"title":"Corporate Equity Performance and Changes in Firm Characteristics","authors":"B. Blank, Cole McLemore","doi":"10.21314/JOIS.2021.007","DOIUrl":"https://doi.org/10.21314/JOIS.2021.007","url":null,"abstract":"While prior equity performance research analyzes portfolio characteristics using multifactor models, portfolio groups are typically used to explain average returns. Instead, we explore annual firm-level data and compare this with annual percentage changes in firm characteristics, emphasizing model predictive power and individual variation. Our analyses show a significant link between individual firm equity returns and percentage changes in total assets, book-to-market ratios, current ratios and shares outstanding, as well as historical returns and average market returns. Our findings affirm prior work illustrating the importance of profitability, size, liquidity, momentum and market returns, although we observe minimal evidence of the importance of investment in capital expenditures. We also perform these analyses at the industry level and note differences across industries, including the cyclical nature of the business equipment and consumer durables industries in contrast to the utilities and energy sectors. Overall, we contribute to the understanding of corporate characteristics and equity performance.","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"75 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90435686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Portfolio management of Commodity Trading Advisors with volatility-targeting","authors":"Marat Molyboga","doi":"10.21314/jois.2020.116","DOIUrl":"https://doi.org/10.21314/jois.2020.116","url":null,"abstract":"","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"1 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67706930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Can shorting leveraged exchange-traded fund pairs be a profitable trade?","authors":"G. Tsalikis, Simeon Papadopoulos","doi":"10.21314/jois.2019.110","DOIUrl":"https://doi.org/10.21314/jois.2019.110","url":null,"abstract":"","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"115 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79067748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper challenges widely applied trading indicators with regard to their ability to generate a robust performance. In this study, we use a semiparametric scenario building approach to simulate artificial price series based on characteristics of the observed price. In addition to testing the trading indicators on the observed price series and holding back some observed data for proforma out-of-sample testing, our price simulations provide a back testing environment to test trading strategies on artificially created prices. This provides an additional performance assessment by allowing us to test the trading indicators for robustness on a large set of artificially created price series with similar characteristics to the observed price series. We find that many trading indicators deliver robust results for certain performance metrics but are unable to deliver robust results and improvements across all reported performance metrics. In addition, most trading strategies influence the statistical moments of the return distribution. While they improve the skewness – and thereby increase the number of positive returns – in most cases, they also increase the kurtosis, introducing undesired additional observations in the tails of the return distributions.
{"title":"Is Trading Indicator Performance Robust? Evidence from Scenario Building","authors":"Andrea Thomann","doi":"10.21314/jois.2020.119","DOIUrl":"https://doi.org/10.21314/jois.2020.119","url":null,"abstract":"This paper challenges widely applied trading indicators with regard to their ability to generate a robust performance. In this study, we use a semiparametric scenario building approach to simulate artificial price series based on characteristics of the observed price. In addition to testing the trading indicators on the observed price series and holding back some observed data for proforma out-of-sample testing, our price simulations provide a back testing environment to test trading strategies on artificially created prices. This provides an additional performance assessment by allowing us to test the trading indicators for robustness on a large set of artificially created price series with similar characteristics to the observed price series. We find that many trading indicators deliver robust results for certain performance metrics but are unable to deliver robust results and improvements across all reported performance metrics. In addition, most trading strategies influence the statistical moments of the return distribution. While they improve the skewness – and thereby increase the number of positive returns – in most cases, they also increase the kurtosis, introducing undesired additional observations in the tails of the return distributions.<br>","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"9 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86823366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data for the period 2013-2018. In line with the theoretical model, our empirical results confirm that both the BitCoin transaction demand and speculative demand have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to the BitCoin velocity, whereas positive shocks to the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.
{"title":"The price of Bitcoin: GARCH evidence from high-frequency data","authors":"P. Ciaian, d'Artis Kancs, M. Rajcaniova","doi":"10.2760/06822","DOIUrl":"https://doi.org/10.2760/06822","url":null,"abstract":"This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data for the period 2013-2018. In line with the theoretical model, our empirical results confirm that both the BitCoin transaction demand and speculative demand have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to the BitCoin velocity, whereas positive shocks to the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"1 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49133705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns and Gaussian dynamic weights or signals, (e.g., linear filters of past returns, such as simple moving averages, exponential weighted moving averages, forecasts from ARIMA models), we are able to derive closed-form expressions for the first four moments of the strategy's returns, in terms of correlations between the random signals and unknown future returns. By allowing for randomness in the asset-allocation and modelling the interaction of strategy weights with returns, we demonstrate that positive skewness and excess kurtosis are essential components of all positive Sharpe dynamic strategies, which is generally observed empirically; demonstrate that total least squares (TLS) or orthogonal least squares is more appropriate than OLS for maximizing the Sharpe ratio, while canonical correlation analysis (CCA) is similarly appropriate for the multi-asset case; derive standard errors on Sharpe ratios which are tighter than the commonly used standard errors from Lo; and derive standard errors on the skewness and kurtosis of strategies, apparently new results. We demonstrate these results are applicable asymptotically for a wide range of stationary time-series.
{"title":"Optimal Dynamic Strategies on Gaussian Returns","authors":"Nikan B. Firoozye, Adriano Soares Koshiyama","doi":"10.2139/ssrn.3385639","DOIUrl":"https://doi.org/10.2139/ssrn.3385639","url":null,"abstract":"Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns and Gaussian dynamic weights or signals, (e.g., linear filters of past returns, such as simple moving averages, exponential weighted moving averages, forecasts from ARIMA models), we are able to derive closed-form expressions for the first four moments of the strategy's returns, in terms of correlations between the random signals and unknown future returns. By allowing for randomness in the asset-allocation and modelling the interaction of strategy weights with returns, we demonstrate that positive skewness and excess kurtosis are essential components of all positive Sharpe dynamic strategies, which is generally observed empirically; demonstrate that total least squares (TLS) or orthogonal least squares is more appropriate than OLS for maximizing the Sharpe ratio, while canonical correlation analysis (CCA) is similarly appropriate for the multi-asset case; derive standard errors on Sharpe ratios which are tighter than the commonly used standard errors from Lo; and derive standard errors on the skewness and kurtosis of strategies, apparently new results. We demonstrate these results are applicable asymptotically for a wide range of stationary time-series.","PeriodicalId":42279,"journal":{"name":"Journal of Investment Strategies","volume":"45 1","pages":""},"PeriodicalIF":0.2,"publicationDate":"2018-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78382487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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