I Made A Huge Mistake In My Published Novel

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I Made A Huge Mistake In My Published Novel

We Are All Most Certainly Going To Make It

Virtually every single thing you have ever been told about luck is wrong. Let me show you what you’ve been missing.

Derivatives of Risk Measures

This paper provides the first and second order derivatives of any risk measures, including VaR and ES for continuous and discrete portfolio loss random variable variables. Also, we give asymptotic results of the first and second order conditional moments for heavy–tailed portfolio loss random variable.

Valuing Pharmaceutical Drug Innovations

We propose a methodology to estimate the market value of pharmaceutical drugs. Our approach combines an event study with a model of discounted cash flows and uses stock market responses to drug development announcements to infer the values. We estimate that, on average, a successful drug is valued at $1.62 billion, and its value at the discovery stage is $64.3 million, with substantial heterogeneity across major diseases. Leveraging these estimates, we also determine the average drug development costs at various stages. Furthermore, we explore applying our estimates to design policies that support drug development through drug buyouts and cost-sharing agreements.

Can Base ChatGPT be Used for Forecasting without Additional Optimization?

This study investigates whether OpenAI’s ChatGPT-3.5 and ChatGPT-4 can accurately forecast future events using two distinct prompting strategies. To evaluate the accuracy of the predictions, we take advantage of the fact that the training data at the time of our experiments (April and May 2023) stopped at September 2021, and ask about events that happened in 2022. We employed two prompting strategies: direct prediction and what we call future narratives which ask ChatGPT to tell fictional stories set in the future with characters that share events that have happened to them, but after ChatGPT’s training data had been collected. Concentrating on events in 2022, we prompted ChatGPT to engage in storytelling, particularly within economic contexts. After analyzing 100 prompts, we discovered that future narrative prompts significantly enhanced ChatGPT-4’s forecasting accuracy. This was especially evident in its predictions of major Academy Award winners as well as economic trends, the latter inferred from scenarios where the model impersonated public figures like the Federal Reserve Chair, Jerome Powell. As a falsification exercise, we repeated our experiments in May 2024 at which time the models included more recent training data. ChatGPT-4’s accuracy significantly improved when the training window included the events being prompted for, achieving 100% accuracy in many instances. The poorer accuracy for events outside of the training window suggests that in the 2023 prediction experiments, ChatGPT-4 was forming predictions based solely on its training data. Narrative prompting also consistently outperformed direct prompting. These findings indicate that narrative prompts leverage the models’ capacity for hallucinatory narrative construction, facilitating more effective data synthesis and extrapolation than straightforward predictions. Our research reveals new aspects of LLMs’ predictive capabilities and suggests potential future applications in analytical contexts.

Quantum Risk Analysis of Financial Derivatives

We introduce two quantum algorithms to compute the Value at Risk (VaR) and Conditional Value at Risk (CVaR) of financial derivatives using quantum computers: the first by applying existing ideas from quantum risk analysis to derivative pricing, and the second based on a novel approach using Quantum Signal Processing (QSP). Previous work in the literature has shown that quantum advantage is possible in the context of individual derivative pricing and that advantage can be leveraged in a straightforward manner in the estimation of the VaR and CVaR. The algorithms we introduce in this work aim to provide an additional advantage by encoding the derivative price over multiple market scenarios in superposition and computing the desired values by applying appropriate transformations to the quantum system. We perform complexity and error analysis of both algorithms, and show that while the two algorithms have the same asymptotic scaling the QSP-based approach requires significantly fewer quantum resources for the same target accuracy. Additionally, by numerically simulating both quantum and classical VaR algorithms, we demonstrate that the quantum algorithm can extract additional advantage from a quantum computer compared to individual derivative pricing. Specifically, we show that under certain conditions VaR estimation can lower the latest published estimates of the logical clock rate required for quantum advantage in derivative pricing by up to ∼ 30x. In light of these results, we are encouraged that our formulation of derivative pricing in the QSP framework may be further leveraged for quantum advantage in other relevant financial applications, and that quantum computers could be harnessed more efficiently by considering problems in the financial sector at a higher level.

Recommender Systems in Financial Trading: Using machine-based conviction analysis in an explainable AI investment framework

Traditionally, assets are selected for inclusion in a portfolio (long or short) by human analysts. Teams of human portfolio managers (PMs) seek to weigh and balance these securities using optimisation methods and other portfolio construction processes. Often, human PMs consider human analyst recommendations against the backdrop of the analyst’s recommendation track record and the applicability of the analyst to the recommendation they provide. Many firms regularly ask analysts to provide a ”conviction” level on their recommendations. In the eyes of PMs, understanding a human analyst’s track record has typically come down to basic spread sheet tabulation or, at best, a ”virtual portfolio” paper trading book to keep track of results of recommendations. Analysts’ conviction around their recommendations and their ”paper trading” track record are two crucial workflow components between analysts and portfolio construction. Many human PMs may not even appreciate that they factor these data points into their decision-making logic. This chapter explores how Artificial Intelligence (AI) can be used to replicate these two steps and bridge the gap between AI data analytics and AI-based portfolio construction methods. This field of AI is referred to as Recommender Systems (RS). This chapter will further explore what metadata that RS systems functionally supply to downstream systems and their features.