{"title":"Predicting Returns with Text Data","authors":"Z. Ke, B. Kelly, D. Xiu","doi":"10.2139/ssrn.3489226","DOIUrl":"https://doi.org/10.2139/ssrn.3489226","url":null,"abstract":"We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs a sentiment score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of sentiment terms via predictive screening, 2) assigning sentiment weights to these words via topic modeling, and 3) aggregating terms into an article-level sentiment score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we text-mine one of the most actively monitored streams of news articles in the financial system|the Dow Jones Newswires|and show that our supervised sentiment model excels at extracting return-predictive signals in this context.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77724159","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":"Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility","authors":"Michael Weylandt, Yu Han, K. Ensor","doi":"10.2139/ssrn.3425531","DOIUrl":"https://doi.org/10.2139/ssrn.3425531","url":null,"abstract":"Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, allowing efficient and robust price discovery, while others are highly illiquid, limiting the effectiveness of standard risk management techniques. We propose a joint modeling strategy, which uses high-frequency information from thickly-traded hubs to improve volatility estimation and risk management at thinly traded hubs. The resulting model has superior in- and out-of-sample predictive performance, particularly for several commonly used risk management metrics, demonstrating that joint modeling is indeed possible and useful. To improve estimation, a Bayesian estimation strategy is employed and data-driven weakly informative priors are suggested. Our model is robust to sparse data and can be effectively used in any market with similar irregular patterns of data availability.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87972782","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":"Empirically-Transformed Linear Opinion Pools","authors":"Anthony Garratt, Timo Henckel, Shaun P. Vahey","doi":"10.2139/ssrn.3412667","DOIUrl":"https://doi.org/10.2139/ssrn.3412667","url":null,"abstract":"Many studies have found that combining density forecasts improves predictive accuracy for macroeconomic variables. A prevalent approach known as the Linear Opinion Pool (LOP) combines forecast densities from “experts”; see, among others, Stone (1961), Geweke and Amisano (2011), Kascha and Ravazzolo (2011), Ranjan and Gneiting (2010) and Gneiting and Ranjan (2013). Since the LOP approach averages the experts’ probabilistic assessments, the distribution of the combination generally differs from the marginal distributions of the experts. As a result, the LOP combination forecasts sometimes fail to match salient features of the sample data, including asymmetries in risk. In this paper, we propose a computationally convenient transformation for a target macroeconomic variable with an asymmetric marginal distribution. Our methodology involves a Smirnov transform to reshape the LOP combination forecasts using a nonparametric kernel-smoothed empirical cumulative distribution function. We illustrate our methodology with an application examining quarterly real-time forecasts for US inflation based on multiple output gap measures over an evaluation sample from 1990:1 to 2017:2. Our proposed methodology improves combination forecast performance by approximately 10% in terms of both the root mean squared forecast error and the continuous ranked probability score. We find that our methodology delivers a similar performance gain for the Logarithmic Opinion Pool (LogOP), a commonly-used alternative to the LOP.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79771792","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":"Dynamic Probabilistic Forecasting with Uncertainty","authors":"F. Benth, Gleda Kutrolli, S. Stefani","doi":"10.2139/ssrn.3405890","DOIUrl":"https://doi.org/10.2139/ssrn.3405890","url":null,"abstract":"In this paper, we introduce a dynamical model for the time evolution of probability density functions incorporating uncertainty in the parameters. The uncertainty follows stochastic processes, thereby defining a new class of stochastic processes with values in the space of probability densities. The purpose is to quantify uncertainty that can be used for probabilistic forecasting. Starting from a set of traded prices of equity indices, we do some empirical studies. We apply our dynamic probabilistic forecasting to option pricing, where our proposed notion of model uncertainty reduces to uncertainty on future volatility. A distribution of option prices follows, reflecting the uncertainty on the distribution of the underlying prices. We associate measures of model uncertainty of prices in the sense of Cont.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75958133","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":"Discovering Market Prices: Which Price Formation Model Best Predicts the Next Trade?","authors":"A. Meyer, Ingo Fiedler","doi":"10.2139/ssrn.3414972","DOIUrl":"https://doi.org/10.2139/ssrn.3414972","url":null,"abstract":"For most purposes of technical analysis, valuation metrics and many other relevant financial methods, the price of the last transaction is considered representative of the market price. The straightforward argument is that at this price, supply and demand have last met. However, on closer examination, the question arises as to why a past event should be relevant to the future, and why other, potentially more recent information should not be used to discover a future price. Building on this question, we apply a range of new price formation models to current data available on crypto currency exchanges that depict level II market data, and compare their short-term forecast accuracy against the common-used ticker price and mid-price. Data on crypto currencies is used as the closest example to free markets, since crypto currency trading is continuous, markets never close, and interferences through oversight is extremely rare. We find that two of the five price formation models investigated outperform the widely used ticker as a price indicator for the next trade. We conclude that the volume-limited clearing price best predicts the price of subsequent trades. Its usage can thus enhance the explanatory power of various financial analyses.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83372550","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":"Regime switches and commonalities of the cryptocurrencies asset-class","authors":"Gianna Figá-Talamanca, S. Focardi, Marco Patacca","doi":"10.2139/ssrn.3388642","DOIUrl":"https://doi.org/10.2139/ssrn.3388642","url":null,"abstract":"In this paper we test for regime changes in the price dynamics of Bitcoin, Ethereum, Litecoin and Monero, as representatives of the cryptocurrencies asset class. Data are observed daily from January, 1, 2016 to October, 15, 2019. Best specifications within Gaussian and Autoregressive Hidden Markov models for price differences are selected through the AIC and BIC information criteria by considering up to four hidden regimes. The empirical results suggest that at most three common states may be considered for the basket of cryptocurrencies under investigation; a fourth state may be relevant as an added factor to the dynamics description of the individual cryptocurrencies rather than to the whole basket. Finally, we test the out-of-sample performance of estimated regime switching models; optimal results, in terms of RMSE and correlation between predicted and real values, are obtained in the case of two common or three individual regimes.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79194522","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":"On Same-Realization Prediction in the Multivariate Long Memory Process with the VAR Procedure","authors":"Wen-Jen Tsay, Cindy S.H. Wang","doi":"10.2139/ssrn.3371610","DOIUrl":"https://doi.org/10.2139/ssrn.3371610","url":null,"abstract":"This paper proposes an easy-to-implement approach to forecasting the multivariate long memory process on same realization and further examines its usefulness on forecasting multivariate volatility series. This procedure bases on the extension of the analysis of Lewis and Reinsel (1985) to the multivariate fractionally integrated model, that is, the vector autoregressive (VAR (<i>k</i>)) model to approximate the multivariate long memory system. Under suitable assumptions on the long memory parameter <i>d</i> and lag length <i>k</i>, the consistency of the multivariate least squares (LS) coefficient estimator and that of the residual covariance matrix estimator Σ̂ <sub>k</sub> are derived. In addition, the one-step ahead prediction error generated by the VAR(<i>k</i>)-approximation model is shown to converge in probability to its population counterpart, even though the exact orders of the multivariate long memory process are <i>unknown</i> and the long memory parameter <i>d</i> varies across each series of the multivariate long memory model. Moreover, insights from our theoretical analysis are confirmed by a set of Monte Carlo experiments, which are consistent with the findings of Lewis and Reinsel (1985) for the short memory process. An empirical application to the multivariate realized and option implied volatility series illustrates the usefulness of our forecasting procedure, when compared to the current volatility forecasting methods.<br>","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73047779","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":"Forecasting of Socio-Economic Security Indicators by Means of Exponential Smoothing","authors":"D. Shvaiba","doi":"10.33619/2414-2948/40/30","DOIUrl":"https://doi.org/10.33619/2414-2948/40/30","url":null,"abstract":"The method of exponential smoothing is widely used in the forecasting of financial and economic characteristics in different sectors of the economy, departments, etc. In the construction of a forecast model by exponential smoothing time series of characteristics of socio–economic security is smoothed with the support of a weighted moving average, in which the weights obey the exponential law. In this case, the following levels of the series are given significant values in comparison with the past, because they carry more important information to determine the predicted values at the level of socio-economic security characteristics.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83729843","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":"Macro Uncertainty and Currency Premia","authors":"Pasquale Della Corte, Aleksejs Krecetovs","doi":"10.2139/ssrn.2924766","DOIUrl":"https://doi.org/10.2139/ssrn.2924766","url":null,"abstract":"This paper studies empirically the relation between macro uncertainty shocks and the cross-section of currency excess returns. We measure uncertainty over macro variables such as current account, ini¬‚ation rate, short-term interest rate, real economic growth and foreign exchange rate using the cross-sectional dispersion of market participants’ expectations from two international surveys of macro forecasts. We i¬ nd evidence that investment currencies deliver low returns whereas funding currencies oi¬€er a hedge when current account uncertainty is unexpectedly high. In contrast, uncertainty over other macro indicators displays no signii¬ cant relation with the cross-section of currency excess returns. Our results are consistent with a recent theory of exchange rate determination based on capital i¬‚ows in imperfect i¬ nancial markets.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84219296","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}
Ana-Maria H. Dumitru, Rodrigo Hizmeri, M. Izzeldin
{"title":"Forecasting the Realized Variance in the Presence of Intraday Periodicity","authors":"Ana-Maria H. Dumitru, Rodrigo Hizmeri, M. Izzeldin","doi":"10.2139/ssrn.3393464","DOIUrl":"https://doi.org/10.2139/ssrn.3393464","url":null,"abstract":"This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted model, HARP, where predictors are built from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000--2016) and via Monte Carlo simulations that the HARP models produce significantly better forecasts, especially at the 1-day and 5-days ahead horizons.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80330145","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}