Stock market volatility forecasting is a task relevant to assessing market risk. We investigate the interaction between news and prices for the one-day-ahead volatility prediction using state-of-the-art deep learning approaches. The proposed models are trained either end-to-end or using sentence encoders transfered from other tasks. We evaluate a broad range of stock market sectors, namely Consumer Staples, Energy, Utilities, Heathcare, and Financials. Our experimental results show that adding news improves the volatility forecasting as compared to the mainstream models that rely only on price data. In particular, our model outperforms the widely-recognized GARCH(1,1) model for all sectors in terms of coefficient of determination @math , @math and @math , achieving the best performance when training from both news and price data.
The works described above ( @cite_7 @cite_42 @cite_32 @cite_30 ) target long-horizon volatility predictions (one year or quarterly @cite_30 ). In particular, @cite_30 and @cite_32 uses market data (price) features along with the textual representation of the 10-K reports. These existing works that employ multi-modal learning @cite_45 are based on a In the setup, text and price features are trained independently and a meta model is used in a later stage to discriminate how to weight the contribution of each mode. approach. For example, stacking ensembles to take into account the price and text predictions @cite_30 . In contrast, our end-to-end trained model can learn the joint distribution of both price and text.
@cite_30 @cite_7 @cite_42 @cite_32 @cite_45
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Volatility clustering, evaluated through slowly decaying auto-correlations, Hurst effect or 1 f noise for absolute returns, is a characteristic property ofmost financial assets return time series (1999). Statistical analysis alone is not able to provide a definite answer for the presence or absence of long-range dependence phenomenon in stock returns or volatility, unless economic mechanisms are proposed to understand the origin of such phenomenon Cont (2005); (1999). Whether results of statistical analysis correspond to longrange dependence is a difficult question and subject to an ongoing statistical debate Cont (2005). Extensive empirical analysis of the financial market data, supporting the idea that the longrange volatility correlations arise from trading activity, provides valuable background for further development of the long-ranged memory stochastic models (2003); (2001). The power-law behavior of the auto-regressive conditional duration process Sato (2004) based on the random multiplicative process and it’s special case the self-modulation process Takayasu (2003), exhibiting 1 f fluctuations, supported the idea of stochastic modeling with a power-law probability density function (PDF) and long-range memory. Thus the agent based economic models Kirman & Teyssiere (2002); Lux & Marchesi (2000) as well as the stochastic models Borland (2004); (2008; 2010); Queiros (2007) exhibiting long-range dependence phenomenon in volatility or trading volume are of great interest and remain an active topic of research. Properties of stochastic multiplicative point processes have been investigated analytically and numerically and the formula for the power spectrum has been derived Gontis & Kaulakys (2004). In themore recent papers (2006); Kaulakys A Ruseckas & Kaulakys (2010) the general form of the multiplicative stochastic differential equation (SDE) was derived in agreement with the model earlier proposed in Gontis & Kaulakys (2004). Since Gontis & Kaulakys (2004) a model of trading activity, based on a SDE driven Poisson-like process, was presented (2008) and in the most recent paper (2010) we proposed a double stochastic model, whose return time series yield two power-law statistics, i.e., the PDF and the power spectral density (PSD) of absolute return, mimicking the empirical data for the one-minute trading return in the NYSE. In this chapter we present theoretical arguments and empirical evidence for the non-linear double stochastic model of return in financial markets. With empirical data from NYSE and Vilnius Stock Exchange (VSE) demonstrating universal scaling of return statistical properties, 27 This paper reports statistical analyses performed on simulated data from a stochastic multi-agent model of speculative behaviour in a financial market. The price dynamics resulting from this artificial market process exhibits the same type of scaling laws as do empirical data from stock markets and foreign exchange markets: (i) one observes scaling in the probability distribution of relative price changes with a Pareto exponent around 2.6, (ii) volatility shows significant long-range correlation with a self-similarity parameter H around 0.85. This happens although we assume that news about the intrinsic or fundamental value of the asset follows a white noise process and, hence, incorporation of news about fundamental factors is insufficient to explain either of the characteristics (i) or (ii). As a consequence, in our model, the main stylised facts of financial data originate from the working of the market itself and one need not resort to scaling in unobservable extraneous signals as an explanation of the source of scaling in financial prices. The emergence of power laws can be explained by the existence of a critical state which is repeatedly approached in the course of the system's development. We present a knowledge discovery-based framework that is capable of discovering, analyzing and exploiting new intraday price patterns in forex markets, beyond the well-known chart formations of technical analysis. We present a novel pattern recognition algorithm for Pattern Matching, that we successfully used to construct more than 16,000 new intraday price patterns. After processing and analysis, we extracted 3518 chart formations that are capable of predicting the short-term direction of prices. In our experiments, we used forex time series from 8 paired-currencies in various time frames. The system computes the probabilities of events such as "within next 5 periods, price will increase more than 20 pips". Results show that the system is capable of finding patterns whose output signals (tested on unseen data) have predictive accuracy which varies between 60 and 85 depending on the type of pattern. We test the usefulness of the discovered patterns, via implementation of an expert system using a straightforward strategy based on the direction and the accuracy of the pattern predictions. We compare our method against three standard trading techniques plus a "random trader," and we also test against the results presented in two recently published studies. Our framework performs very well against all systems we directly compare , and also, against all other published results. Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit "exposure bias" - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries.