Mimicking and learning the long-term memory of efficient markets is a fundamental problem in the interaction between machine learning and financial economics to sequential data. Despite the prominence of this issue, current treatments either remain largely limited to heuristic techniques or rely significantly on periodogram or Gaussianty assumptions. In this paper, we present the ApeRIodic SEmi-parametric (ARISE) process for investigating efficient markets. The ARISE process is formulated as an infinite-sum function of some known processes and employs the aperiodic spectrum estimation to determine the key hyper-parameters, thus possessing the power and potential of modeling the price data with long-term memory, non-stationarity, and aperiodic spectrum. We further theoretically show that the ARISE process has the mean-square convergence, consistency, and asymptotic normality without periodogram and Gaussianity assumptions. In practice, we apply the ARISE process to identify the efficiency of real-world markets. Besides, we also provide two alternative ARISE applications: studying the long-term memorability of various machine-learning models and developing a latent state-space model for inference and forecasting of time series. The numerical experiments confirm the superiority of our proposed approaches.

Deep learning technology has rattled the global financial industry in both positive and negative ways. On the one hand, deep learning technology has considerably improved market efficiency. Tomiwa, a big data author and expert, claims to have beaten the stock market average over the past ten years with a program that he developed with Python. The same kind of program could be used by Forex or derivative traders. One of the biggest downsides, though, is that it has giving larger institutional traders with deep pockets an even stronger advantage.

About two years ago I wrote a little piece about applying Reinforcement Learning to the markets. It was a project I had worked on for a while in various forms. A few people asked me what became of it. So this post covers some high-level things I've learned. It's more of a rant than an organized post, really. If there is enough interest in this topic I'd be happy to go into more technical detail in future posts, but that's TBD.

Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers. Lastly, we show that our model's performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008.

I can still remember reading about artificial intelligence when I was learning to write C code at the tender age of 15. If you asked me how artificial intelligence would change our lives, I would talk about the impact of video games or the future of robotics. The thought that AI could be used for stock trading never crossed my mind. I have recently observed that AI has changed the world of financial trading in ways that I never envisioned. Here are some things that stock traders need to be aware of in 2018.

The conventional wisdom of how most of us should invest our money is clear--avoid paying high fees to money managers for their supposed stock-picking expertise. In fact, steer clear of single stocks altogether, and simply buy "the market," meaning an exchange-traded or mutual fund that passively tracks the performance of the entire stock market. And, maybe most important, focus on the long run, by holding investments through their ups and downs rather than trying to time the market by buying low and selling high--too tricky to do, say the experts. This is good advice, as far as it goes. On the other hand, a big pillar supporting it is the "efficient markets hypothesis," economist-speak for the assumption that the prices of tradable assets like stocks, bonds, and commodities respond immediately and appropriately to new information, an assumption that depends on market participants, in other words people, acting rationally.

We're sitting here scratching our heads when we see that in 2016, the single best performing stock was NVIDIA Corporation (NASDAQ:NVDA) with the NVDA stock price seeing a 224% increase in 2016. Why are we so puzzled? It's because we believe in the "efficient market hypothesis". There's this notion in finance called the "efficient market hypothesis" and while that may sound terribly boring, what it actually means is this. For any given company on the stock market, all the relevant information about said company is already incorporated into the share price.

We use quantitative media (blogs, and news as a comparison) data generated by a large-scale natural language processing (NLP) text analysis system to perform a comprehensive and comparative study on how company related news variables anticipates or reflects the company's stock trading volumes and financial returns. Building on our findings, we give a sentiment-based market-neutral trading strategy which gives consistently favorable returns with low volatility over a long period. Our results are significant in confirming the performance of general blog and news sentiment analysis methods over broad domains and sources. Moreover, several remarkable differences between news and blogs are also identified.