Evaluation of Machine Learning impact on Asset Risk Premia measurement

1. Introduction Predicting stock market movements has always been an issue of great interest for investors. The predictability and the profitability are strictly linked together, and thus enhancing the predictive capabilities can lead to an advantage to be exploited by adjusting investment stra- tegies while minimising risk. Recent improvements in the field of machine learning, with their ability to approximate challenging tasks, continue to question the unbeatability of the market, and provide a world of innovative solutions for improved predictability in the measu- rement of excess return. The analysis of stock prediction, nevertheless, can only be undertaken in conjunction with the definition of time series analysis. A time series is a sequence of observations ordered in time. The type of sequence changes depending on whether we are dealing with a continuous time series, i.e. a system where values change continuously during a time interval, and a discrete time series, i.e. a system with a finite set of values. In machine learning classification prob- lems, values are discrete, while in a machine learning regression problem, the variable is con- tinuous. The trend of a series can still be characterized by possible seasonal variations and by station- ary or non-stationary trends. While in the stationary, mean and variance do not change over time, a stationary trend (in our case the stock market trend) has mean and variance that are not constant. More broadly, a motion can be defined not constant in the presence of trends, which are essential when it comes to business, industry or general economic phenomena. Moreover, it is very crucial to take into account all the "noisy processes", i.e. unexpected news that can come from different events, such as technological innovations, political changes, natural disasters, that change from one moment to another and modify the possible performance of a stock. For this very reason, one of the objectives of this work is to under- stand how machine learning systems can deal with a series of predictable and unpredictable events, and how much they are able to explain these market movements in advance. Therefore, the paper will precisely review the state of the art regarding the techniques em- ployed in the field of machine learning, the empirically demonstrated predictive performances from recent literature, and the future applications. Explaining more precisely what machine learning is, it would be defined as the ability of a system to learn through experience. A series of data are entered into the system, of which the algorithm must map the behaviours, to be applied later on to a new series of "unseen data". Predictive abilities should then be evaluated by comparing the values returned by the al- gorithm and the actual values expected by the researcher. The smaller the measurement of the prediction error, the greater the possibility of approximation and reliability of the system. The term machine learning used in this thesis refers to regression algorithms, and is used with ref- erence to a series of predictive statistical models, which combined with systems of "regulariz- ation" are useful to reduce data overfitting and extract from a complex set of data, patterns and insights useful for the researcher. Their main characteristic, which distinguishes them from classical econometric methods, is the ability to be flexible and to manage large amounts 1