The Alpha Engine: Designing an Automated Trading Algorithm
On Feature Reduction using Deep Learning for Trend Prediction in Finance abstract One of the major advantages in using Deep Learning for Finance is to embed a large collection of information into investment decisions. A way to do that is by means of compression, that lead us to consider a smaller feature space. Several studies are proving that non-linear feature reduction performed by Deep Learning tools is effective in price trend prediction. The focus has been put mainly on Restricted Boltzmann Machines (RBM) and on output obtained by them. Few attention has been payed to Auto-Encoders (AE) as an alternative means to perform a feature reduction. In this paper we investigate the application of both RBM and AE in more general terms, attempting to outline how architectural and input space characteristics can affect the quality of prediction.
Forecasting a Volatility Tsunami
Algorithmic Trading in Limit Order Books for Online Portfolio Selection
Fast LP Algorithms for Portfolio Optimization
Toxic Arbitrage and Price Discovery
Quantization Meets Fourier: A New Technology for Pricing Options
SenSR: A Sentiment-Based Systemic Risk Indicator
Markets' Notion on Implied Volatility Risks: Insights from Model-Free VIX Futures Pricing
Momentum in Traditional and Cryptocurrencies Made Simple
Time-Varying Risk Premiums and Economic Cycles
Reply to 'Comment on ‘Markowitz Versus Michaud: Portfolio Optimization Strategies Reconsidered’'
Hedge and Speculate: Replicating Option Payoffs with Limit and Market Orders
An empirical behavioural order-driven model with price limit rules abstract We develop an empirical behavioural order-driven (EBOD) model, which consists of an order placement process and an order cancellation process. Price limit rules are introduced in the definition of relative price. The order placement process is determined by several empirical regularities: the long memory in order directions, the long memory in relative prices, the asymmetric distribution of relative prices, and the nonlinear dependence of the average order size and its standard deviation on the relative price. Order cancellation follows a Poisson process with the arrival rate determined from real data and the cancelled order is determined according to the empirical distributions of relative price level and relative position at the same price level. All these ingredients of the model are derived based on the empirical microscopic regularities in the order flows of stocks on the Shenzhen Stock Exchange. The model is able to produce the main stylized facts in real markets. Computational experiments uncover that asymmetric setting of price limits will cause the stock price diverging exponentially when the up price limit is higher than the down price limit and vanishing vice versus. We also find that asymmetric price limits have influences on stylized facts. Our EBOD model provides a suitable computational experiment platform for academics, market participants and policy makers.
Divest, Disregard, or Double Down?
On a Constructive Theory of Financial Markets (Prologue)
Dissecting Characteristics Nonparametrically
The Economics of the Fed Put
Bartlett's delta in the SABR model abstract We refine the analysis of hedging strategies for options under the SABR model carried out in . In particular, we provide a theoretical justification of the empirical observation made in  that the modified delta ("Bartlett's delta") introduced there provides a more accurate and robust hedging strategy than the conventional SABR delta hedge.
The Financial Economics of White Precious Metals - A Survey
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