Stay Ahead Of The Curve: AI Weekly. Trading Fast and Slow When it comes to trading fast, high-frequency trading, as you can tell by the name, is pretty much the definition of it. This approach sees computers follow complex algorithms as they conduct dozens of gargantuan transactions every second. For the traders, this means accumulating small, even minuscule, margins into large gains, and for the trading platform, this means an infusion of liquidity. The latter often allows HF-traders to save on dragdown expenses like fees or commissions. The first problem is that an HFT operation comes with a lot of expenses. The huge data centers filled to the brim with powerful computers crunching the numbers as they execute the complex algorithms take a lot of power to run. That is the smaller of the issues, though, as the competition puts HF-traders under a lot of pressure to constantly upgrade both the hardware and software they are using, which also leads to enormous bills. This means that HFT as such is a domain of large institutional investors, which fuels the controversies around the strategy. Add to this the increasing government scrutiny, and you have a whole load of issues biting into your profit margins – and those were small to begin with, remember? This is why trading fast is not an option for private investors, and for smaller institutionals as well. What are the other options on the table? Let’s look. Read more. Building an AI-based Algorithmic Trading Strategy For decades, trading has been done primarily by humans, some of them crowding at the busy exchange, others picking out the best assets to buy and sell from the office. Today, however, a major share of market transactions is in fact conducted by sophisticated algorithms. In fact, just a few years ago, algorithmic trading was reportedly behind 60-70% of transactions on US financial markets, although their current share is probably lower. In itself, however, algorithmic trading is not necessarily something particularly new: in fact, the widely spoken-about practice known as High-Frequency Trading, one of the prime examples of top-notch algorithmic strategies, stems from the early 2000s. What is new these days, however, is a fintech trend that holds a promise of amplifying the success of algo-traders by giving them extra tools to improve the performance of their models.



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The AI Gold-Mine: Predicting Stock Market Success Yaron Golgher, CEO and Co-founder of I Know First, participated in an interview prior to the Deep Learning in Finance Summit on 27–28 April, along with Lipa Roitman, the company’s CTO. Here are some highlights: The main challenge is to make the customers feel comfortable with the outputs of the system and to make it clear to them that it’s not going to be the case as e.g. with traditional econometric models that they’ll be able to follow or comprehend the reasoning behind those outputs. Because this is precisely the idea here behind the application of deep learning: it’s just too complex, too dynamic and too many parameters/features play a role making it impossible for a human brain to digest and analyze it. When it comes to the execution based on the insights given by the model, it’s not the question of “why” the system recommends this and that, but rather whether you might want to add an additional “human” filter on top of it before you follow the recommendations.

With deep learning technology becoming more popular in the trading industry when it comes to finding the most promising trade ideas and gaining the edge, we expect a much broader range of uncorrelated strategies to be created as well as the respective funds in the future applying it. With growing amounts of all kinds of data that can have predictive value, deep learning is literally opening so many doors to discover new things and we’re very excited to contribute to and to see what is going to happen in the investment industry in the next few years. Read more. Are Markets as Chaotic as We Think? The rise of machine learning and the omnipresent AIs poses quite a variety of questions for humanity as it enters the era when everything around has the word “smart” in its name. How do we make sure that smart machines do not leave humans out of jobs and, thus, means to sustain themselves? How do we maintain people’s privacy in a time when data, the AI fuel, is such a major business enabler? Will we ever get to the point where advanced mathematics can approximate the human cognition, with all of its virtues and drawbacks, and how do we remain human in the age of singularity? These are all good and valid questions to mull over, but there are also dozens and dozens of more pragmatic things to consider.



One of them is the way the AI technology is transforming the financial sector, and here, one of the most difficult, yet most alluring challenges for machine learning is this: can AI be used for stock market prediction with an efficiency that would leave the conventional investment strategies behind? Can AIs beat the stock market?



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FB Stock Predictions: Stock Forecast Evaluation Report In this forecast evaluation report, we examine the performance of the stock market predictions generated by the I Know First AI Algorithm for the FB stock with time horizons ranging from 3 days to 3 months, which were delivered daily to our clients. Our analysis covers the time period from 1 January 2019 to 9 July 2019. Below, we present our key takeaways for checking hit ratios of our FB predictions in the stock market.



Highlights: 95% Hit Ratio for 3-months time period for FB predictions allowing our clients to be able to invest their money with significant less risk

Predictions consistently hit above 58% accuracy despite volatile market conditions



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