Various Trading Strategies
Greek Risk Management
Investing risks can be influenced by a number of factors. These factors can either help or hurt the investment, depending on the type of position established.
To become a successful investor, it is essential to understand what factors influence our investment. The "Greek Risk Measures", a set of risk measures - DELTA, THETA,VEGA, and GAMMA, indicate how exposed our investment position is to time-value decay, and imply volatility and changes in the underlying price.
AirU Alternative Position-Sizing System (AAPSS)
In the stock market, determining the proper position size before a trade can have a very positive impact on our trading results. Determining a proper position size always comes from a good Risk-Sizing System. We are proud to say that AirU has developed a Risk-Sizing System to optimize position size when facing these risks in the market.
By integrating quantitative and fundamental analysis using a technique called Integrative Process, we have developed a high frequency algorithm that translates theoretical to practical performance. Matrix-analysis of various data points - some of which include over 2,800 large/mid/small cap stocks, growth prospects, alpha trends, market multiples, long-term investment results - have proven our approach to be successful.
The inherent strength of our process and its consistent application over time has led to winning new mandates, as well as additional allocation of significant assets.
In general, an equity market-neutral strategy should eliminate market delta risk, generating absolute returns, while largely being independent of the market's performance.
Investing in Relationships
An equity market-neutral strategy seeks to generate returns by exploiting equity market inefficiencies. It involves simultaneously holding long and short equity portfolios of approximately the same delta amount. This involves buying attractive stocks (the long position of the portfolio), and selling unattractive stocks (the short position of the portfolio). The spread between the performance of the long and short positions provide the primary return for this strategy.
Approaches to Equity Market-Neutral
In the market, there are two basic approaches to being equity market-neutral: Statistical Arbitrage and Fundamental Arbitrage. Many successful managers blend these two techniques, depending on market conditions and/or their expertise.
Statistical arbitrage involves model-based, shortterm trading using quantitative and technical analysis to detect profit opportunities. The manager hopes to discover a persistent and statistically significant method to detect profit opportunities.
The leverage used for this type of approach is higher, and typically depends on the number of positions in the portfolio, the desired liquidity, and the risk budget. Normally, Pairs-Trading, Stub-Trading, and Multiclass-Trading are of this approach.
In fundamental arbitrage, the manager or analyst main uses fundamental factors (e.g. price/earnings, price/cash flow, price/earnings before interest and tax, price/book, discounted cash flows, return on equity, operating margins and other indicators) and quantitative data to build portfolios. Usually, this involves buying and/or selling certain industries or companies.
Trading with Smart AI
Transactions, risk calculations, trading strategies, and position size of every stock in Ark Show's net assets are all determined automatically by AI. Ark Show works based on market timing and long short equity trading styles of hedge funds.
Using multiple analytical processes and calculations, it is capable of innovating and even developing its own trading strategies.
Ark Show applies Natural Language Processing (NLP) as part of its analysis process. Ark Show identifies and processes content related to the stock market, including news, articles and reports. It then transfers the data into its database, analyzing it and creating a decision tree to understand the benefits of particular stocks. It also analyzes the degree of fluctuation, and types of stocks connected to a particular subject mentioned in a report, and decides how it may affect stock prices by matching comparisons with the trends of stock prices.
We apply the Viterbi algorithm and Hidden Markov models to enable our AI to review and learn financial terms. Our AI has an expansive dictionary database, giving it the ability to recognize over 80,000 financial terms. This allows it to understand and identify useful information.
Chinese Natural Language Processing（NLP）
We have built a dictionary database of over 80,000 financial terms for Ark Show's NLP (Natural Language Processing). This enables it to understand basic contents of news, reports, and articles about the stock market. It also enables it to make a correlation between these articles and how they may affect the stocks.
Ark Show utilizes NLP in its analytical process. After going through extensive data sources, Ark Show identifies and processes the contents of related news, articles, and reports. It then transfers the data into its database for further analysis.
Big Data Algorithms
We developed Ark Show with the intention of saving time for our investors. It is backed with algorithms combining big data with machine learning AI. It also incorporates hedge fund trading strategy, and performs automated data mining. Using Web Crawling technology to analyze new articles, Ark Show makes prediction and risk calculations, simultaneously expanding its own financial dictionary.
Ark Show integrates data mining technology to parse thousands of source material and uses Natural Language Processing (NLP) to analyze information before making trading decisions.
Data Mining & Web Crawling
In order to improve Ark Show's analytical ability and reliability, we apply data mining technology, enabling Ark Show to crawl across and analyze a large number of reports, reviews, and news on the stock market.
Using our data mining technology, we are able to stably parse more than 26 websites for useful information. These analytics happen on a 24/7 basis at a high frequency, separating different data into their respective databases on its own using various data cleansing techniques and data classification processes.Download