Artificial Intelligent Strategy In Investment

Artificial Intelligence in Investing
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Background information
In the digital era, transformational technology is powering new forms of automation that are more universal and smarter than ever before. The technology have not only changed significant operational aspects such as logistics, manufacturing, and warehousing but also the investment industry at large. Technologies associated with artificial intelligence help in performing tasks commonly associated with human beings such as logical operations, visual operations, speech recognition, visual perceptions, language translation and decision-making. Today,Computer technology has the capabilities of trouncing human intelligence in solving complex computations in shorter periods. The same technique can be replicated in investment in problem-solving, decision making and risk management. For instance, artificial intelligence tools helps in making faster precise assessment of potential borrowers at minimal cost which accounts for better informed and data backed decision that reduces human bias.
Investing on product portfolios backed with reliable information is an important aspectthat helps yield profitability. The increasingly advancement incomputer technology and algorithms have improved the way money managers invest (TG. 2014). The digital revolution is transforming the asset management industry as well as the digitized data increasingly accelerating the demand for artificial computing engine. Today, portfolio managers depend on the available data in prevailing markets to make investment decisions. Artificial intelligence is considered more efficient than human capacity in sorting, analyzing massive amounts either structured or unstructured data (Price, 2018).
According toPrice (2018), Artificial intelligence is the capability of a machine to demonstrate human-like intelligence and a degree of independent learning. For financial organization, on both the buy side and sell side, artificial technologies alter the industry that has long been stuck on its ways with significant risks and opportunities. Firms from buy side and sell side are using artificial intelligence to execute trades, manage portfolios and service their clients(Bunz &Janciute, 2018). Today, these companies utilize artificial intelligence to improve the analysis of securities to make investment decisions and also improve the operational processes. Artificial intelligence help improves efficiency, reduction of errors and increase the probability of better predictions for the investment industry. In the future, artificial intelligence will revolutionize how investment information is analyzed, packaged and presented to the investors(Berke et al, 2016).
Currently, algorithmic high frequency trading have has been on the rise in the recent past. For example, in the year 2018, over two trillion dollar were traded through artificial intelligence platforms. Typically, intelligent trading system process both structured and unstructured data for shorter period than human beings would take. In trading, time is critical element, hence, faster data processing translates to faster decisions and more swift transactions. Moreover, predictions over these tools on stock performance are more accurate as algorithms can test trading systems based on the past data and bring the validation process to a whole new level. Also, artificial intelligence combines recommendations for the strongest portfolios depending on an investors short and long-term goals. For example, Bloomberg initiated Alpaca Forecast artificial prediction matrix, which is a price forecasting application for investors powered by artificial intelligence. The tool integrates real time market data and advanced learning engine to identify price pattern movements for high accuracy market predictions.
Today, most of the organizations have digitized vast amounts of data and continuously use the information to serve their clients. However, with increased investment data, managers find it hard to analyze such massive data even with the help of complex spreadsheets and sophisticated visual basic applications. Artificial intelligence looks to the critical mass of data to learn and develop insights based on this information (Price, 2018). For instance, analyzing financial statements for multiple companies for ten years can be an uphill task for management and researchers; however, with the use of artificial intelligence, timely and accurate information can be achieved.
Additionally, artificial intelligence improvesbusiness outcomes by accelerating innovation and leverage emerging technologies to operate in more efficiently, manage risks and inspire confidence (Grose, 2016). For example, in the banking industry, smart chat bots powered by artificial intelligence, offers clients a comprehensive self-help solutions, therefore, reducing workload in the call centres. Moreover, Amazon Alexa voice controlled virtual assistants powered by artificial technology offers improved services in checking balances, tracking account activity and schedule payments.
Problem statement
The modern investment industry has rapidly evolved, although some people still hold perceptions that the primary concepts of investments have not changed, the reality is that it has changed and most investors and management are facing more challenges than in the past years. The most challenging aspects investor face today is the change in speed and volume of data. In the past, investment data was readily available on the annual and quarterly reports. These publications collected business information and spread valuable information to potential investors at a print speed (Lopez, 2007). Today, these publications and manuals are considered inadequate as the trading market have incorporated many players in the market who consistently distribute a stream of data ranging from the daily price fluctuations to the available investment portfolios. Typically, when information originates from different sources, it becomes challenging for investors and management to determine which information is essential (Papanikolaou, 2011).
Moreover, finding the right resources is often tied up to too much information. Commonly it is a demanding task for an investor to find an excellent resource amidst the increased product portfolios and players in the market. Although having improved access to excellent free information is beneficial to the investor, it can sometimes make the research more daunting because of all the choices. Facts inform significant investing decisions; however, many investors find it hard to determine whether the technical matter more than the fundamentals. Universally, many investors learn to distinguish accurate information and reliable sources that match investing tastes with time. However, until then, it is difficult to evade being overwhelmed with investment portfolios in the market.
Besides, the current market has increasing become reactionary. Even if an investor had access to reliable information, they would still be prone to inaccurate information or unforeseen uncertainty in the market.Erroneous information can be in the form of malicious rumors, human-determined mistakes, and financial scheme on significant corporations in the market Even though the response mechanism is quick, incorrect information always hits the market hard. (Papanikolaou, 2011)
Purpose of the study.
The study seeks to explore how artificial intelligence strategies help an investor to make informed investment decisions and the risks of using synthetic approach.
Research objectives
The research study is designed to assess how artificial intelligence strategies can help investors in making a better decision and the risks factors in investing in synthetic intelligence strategies.
Artificial intelligence in investment
Artificial intelligence is a field in computer science that looks to develop intelligent machines that can imitate human intellectual and learning. It focuses on the use of neural networks along with learning methods in identifying and analyzing economic factors to develop profitable models.There have been an increase in computer power and data storage over the years that are causing a rise in these artificial intelligence systems that surpasses human capabilities. Today, machine learning have made strides in the financial services worldwide such as insurance firms, trading firms, and insurance firms.
The conventional computer programming techniques popularly used in artificial intelligence techniques include, knowledge-based techniques, machine learning technique, and sensory or motor techniques such as natural language and image processing. For instance, expert systems have been used to analyzed past data and predict a corporation future performance. Moreover, the neural networks have been used to generate buy and sell decisions on stock indices. On the other hand, natural language processing languages are used in analyzing the corporate news releases and suggest a buy or sell signal for the corporate stock. Artificial intelligence has been used in assets valuation but is also applicable to data identification and risk management (Price et al, 2018).For example, Greenkey Technologies a Chicago based firm utilizes speech recognition and nat...