Financial markets are nonlinear with complexity, where different types of assets are traded between buyers and sellers, each having a view to maximize their Return on Investment (ROI). Forecasting market trends is a challenging task since various factors like stock-specific news, company profiles, public sentiments, and global economic conditions influence them. This paper describes a daily price directional predictive system of financial instruments, addressing the difficulty of predicting short-term price movements. This paper will introduce the development of a novel trading system methodology by proposing a two-layer Composing Ensembles architecture, optimized through grid search, to predict whether the price will rise or fall the next day. This strategy was back-tested on a wide range of financial instruments and time frames, demonstrating an improvement of 20% over the benchmark, representing a standard investment strategy.

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Index funds are substantially preferred by investors nowadays, and market sensitivities are instrumental in managing index funds. An index fund is a mutual fund aiming to track the returns of a predefined market index (e.g., the S&P 500). A basic strategy to manage an index fund is replicating the index's constituents and weights identically, which is, however, cost-ineffective and impractical. To address this issue, it is required to replicate the index partially with accurately predicted market sensitivities. Accordingly, we propose a novel partial-replication method via learning to predict market sensitivities. We first examine deep-learning models to predict market sensitivities in a supervised manner with our data-processing methods. Then, we propose a partial-index-tracking optimization model controlling the net predicted market sensitivities of the portfolios and index to be the same. These processes' efficacy is corroborated by our experiments on the Korea Stock Price Index 200. Our experiments show a significant reduction of the prediction errors compared with historical estimations and competitive tracking errors of replicating the index utilizing fewer than half of the entire constituents. Therefore, we show that applying deep learning to predict market sensitivities is promising and that our portfolio construction methods are practically effective. Additionally, to our knowledge, this is the first study addressing market sensitivities focused on deep learning.

Artificial intelligence ('AI') is more and more sneaking up into our daily activities. Anyone using Google, Facebook or a Microsoft product knows this. Not every enterprise is using AI at the same pace. Has your organization started looking into using AI yet? Do you have any clue on how to tackle and implement AI in your organization?

Due to the rapid pace of technological change, the way we trade the stock market is becoming more complex. One of the most significant changes that have occurred is the emergence of algorithmic trading, which has allowed traders to improve their skills and compete against other individuals. This type of trading has also raised the bar for other types of traders and is poised to outstrip traditional methods. According to a working paper released by the UK Government's Foresight panel, which Dame Clara Furse chairs, high-frequency trading will eventually replace human decision-making in the stock markets. Algorithmic trading is a type of financial transaction that uses computers and programs to generate and execute large orders in the market.

The current interest rate decision is casting its shadow ahead, and the horrendously rising inflation rates are unsettling market participants. In the fight against inflation, the US Federal Reserve will likely shrink its balance sheet further and herald more aggressive interest rate steps. However, there is then a risk of significantly weakening the economy. That would seriously worsen the macroeconomic picture and significantly increase the risk of the US sliding into recession. The biggest losers from a major interest rate hike will likely remain interest rate sensitive growth stocks. However, such a scenario is already priced in for many stocks.

It was undoubtedly one of the outperformers in the first month of the current stock market year. The shares of the Australian company BrainChip Holdings, which also has subsidiaries in the US, India and France, almost quadrupled from AUD 0.71 to AUD 2.25 within one month. The announcement that Mercedes intends to develop systems based on BrainChip's Akida hardware and software caused a veritable buying panic. Among other things, the technology makes the "Hey, Mercedes" voice control in the EQXX five to ten times more efficient than conventional voice control. Since February, the stock has been in a strong consolidation phase, which is not unusual for such an innovative technology company.

In November 2021, Oluwaseun Medayedupin was arrested by the Nigerian police in Lagos. An investigation found that he had been pursuing "disgruntled employees" from American companies and pushing them to release ransomware on internal enterprise servers, offering a percentage of the cut if they agreed to collaborate in the attack. This was a sophisticated social engineering scheme, far more advanced than the notorious "Nigerian prince" emails that have made the country of Nigeria synonymous with scams. The origins of these types of scams may be attributed to a boom in the establishment of cybercafes during the 1990s, coinciding with falling oil prices in Nigeria and a rise in unemployment. Add in a lack of national social security, and many Nigerians were forced to seek out alternative forms of employment--physical labor; gig work; and, most notoriously, cybercrime.

Algorithmic trading can be fun and rewarding. To the unaware, it refers to trading based on pre-programmed instructions instead of human sentiment. The idea is to leverage computers' superior speed and analytical abilities relative to humans. Algorithmic trading has gained a lot of popularity with retail and institutional traders. The algorithmic trading market is still increasing, with an expected compound annual growth rate (CAGR) of 11.23% between 2021 to 2026 (Mordor Intelligence).

The topic of crypto regulation has been raised for many years in a row since the authorities saw the huge growing popularity of alternative financial instruments. Some countries are trying to ban crypto transactions, while others, on the contrary, give the green light. But it is impossible to say that the world has formed a clear legislative base on this issue. Cryptocurrency laws change almost every year, making the owners of crypto assets nervous. The issue is especially acute for companies that invest in or accept cryptocurrencies from clients.