Towards Explainable AI: Significance Tests for Neural Networks

Horel, Enguerrand; Giesecke, Kay

doi:10.2139/ssrn.3335592

Cited by 22 publications

(27 citation statements)

References 29 publications

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“…We rank the importance of firm-specific and macroeconomic variables for the pricing kernel based on the sensitivity of the SDF weight ω with respect to these variables. Our sensitivity analysis is similar to Sirignano et al (2016) and Horel and Giesecke (2019) and based on the average absolute gradient. More specifically, we define the sensitivity of a particular variable as the average absolute derivative of the weight w with respect to this variable:…”

Section: F Variable Importancementioning

confidence: 99%

“…In order to deal with the infinite number of moment conditions we extend the classcial GMM setup of Hansen (1982) and Chamberlain (1987) by an adversarial 3 Other related work includes Sirignano et al (2016) who estimate mortgage prepayments, delinquencies, and foreclosures with deep neural networks, Moritz and Zimmerman (2016) who apply tree-based models to portfolio sorting and Heaton et al (2017) who automate portfolio selection with a deep neural network. Horel and Giesecke (2019) propose a significance test in neural networks and apply it to house price valuation.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning in Asset Pricing

Chen¹,

2019

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We propose a novel approach to estimate asset pricing models for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. Our general non-linear asset pricing model is estimated with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. We estimate the stochastic discount factor that explains all asset returns from the conditional moment constraints implied by no-arbitrage. Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors. We trace its superior performance to including the no-arbitrage constraint in the estimation and to accounting for macroeconomic conditions and non-linear interactions between firm-specific characteristics. Our generative adversarial network enforces no-arbitrage by identifying the portfolio strategies with the most pricing information. Our recurrent Long-Short-Term-Memory network finds a small set of hidden economic state processes. A feedforward network captures the non-linear effects of the conditioning variables. Our model allows us to identify the key factors that drive asset prices and generate profitable investment strategies.

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Section: F Variable Importancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning in Asset Pricing

Chen¹,

2019

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“…Let us stress that our trading system is not a black box; the logic of its decisions concerning trading stocks (any instruments) can be fully reconstructed and understood; cf. (Horel and Giesecke 2019). We found not many situations where its decisions could be questioned on the basis of the usual technical analysis, though the system uses the stockcharts and its own prior decisions in novel ways.…”

Section: The Key: Bidding Tablesmentioning

confidence: 93%

“…We note that the trades of our system are fully explainable; it is not a "black box". Only such AI can be really trustworthy; see, e.g., (Horel and Giesecke 2019).…”

Section: Momentum Investingmentioning

confidence: 99%

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Artificial Intelligence Approach to Momentum Risk-Taking

Cherednik¹

2021

IJFS

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We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management focused on short-term volatility. Its implementation, a fully automated momentum equity trading system, is systematically discussed in this paper. It proved to be successful in extensive historical and real-time experiments. Momentum risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning. We begin with a new mathematical approach to news impact on share prices, which models well their power-type growth, periodicity, and the market phenomena like price targets and profit-taking. This theory generally requires Bessel and hypergeometric functions. Its discretization results in some tables of bids, basically, expected returns for main investment horizons, the key in our trading system. A preimage of our approach is a new contract card game. There are relations to random processes and the fractional Brownian motion. The ODE we obtained, especially those of Bessel-type, appeared to give surprisingly accurate modeling of the spread of COVID-19.

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Reinforcement learning and stochastic optimisation

Jaimungal

2021

Finance Stoch

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At the heart of financial mathematics lie stochastic optimisation problems. Traditional approaches to solving such problems, while applicable to broad classes of models, require specifying a model to complete the analysis and obtain implementable results. Even then, the curse of dimensionality challenges the viability of conventional methods to settings of practical relevance. In contrast, machine learning, and reinforcement learning (RL) particularly, promises to learn from data and overcome the curse of dimensionality simultaneously. This article touches on several approaches in the extant literature that are well positioned to merge our traditional techniques with RL.

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Towards Explainable AI: Significance Tests for Neural Networks

Cited by 22 publications

References 29 publications

Deep Learning in Asset Pricing

Deep Learning in Asset Pricing

Artificial Intelligence Approach to Momentum Risk-Taking

Reinforcement learning and stochastic optimisation

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