Pinned Repositories
Deep-Learning-in-Asset-Pricing
I follow the steps described in Chen, Pelger, & Zhu (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350138): Assets are priced with a Stochastic Discount Factor, and test assets are selected using an Adversarial Network.
Exuberance_in_Financial_Markets-_Deep_Learning
Support Vector Machines and Neural Networks are used to predict low return periods in financial markets. Price to Book ratio and Dividend yield from the MSCI World index are used as inputs, the index level is used for classification. The lowest 25% percentile is the threshold for a low return classification.
fmp_fundamental_prediction
I load data from https://site.financialmodelingprep.com/ and apply a random forest model to classify high or low returns. The classification works well, but still underperforms a Buy and Hold, as outliers to the right are missed.
vincentweiss96's Repositories
vincentweiss96/Deep-Learning-in-Asset-Pricing
I follow the steps described in Chen, Pelger, & Zhu (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350138): Assets are priced with a Stochastic Discount Factor, and test assets are selected using an Adversarial Network.
vincentweiss96/Exuberance_in_Financial_Markets-_Deep_Learning
Support Vector Machines and Neural Networks are used to predict low return periods in financial markets. Price to Book ratio and Dividend yield from the MSCI World index are used as inputs, the index level is used for classification. The lowest 25% percentile is the threshold for a low return classification.
vincentweiss96/fmp_fundamental_prediction
I load data from https://site.financialmodelingprep.com/ and apply a random forest model to classify high or low returns. The classification works well, but still underperforms a Buy and Hold, as outliers to the right are missed.