Acquisition by Ashok Mishra of 100 shares of Innodata at 2.45 subject to Rule 16b-3

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Published over a year ago View all stories for Innodata | Innodata Hype Analysis

Acquisition of 100 common stock at 2.45 of Innodata by Ashok Mishra on 27th of April 2024. This event was filed by Innodata with SEC on 2021-09-01. Statement of changes in beneficial ownership - SEC Form 4

Cash Flow Correlation

Innodata's cash-flow correlation analysis can be used to evaluate the unsystematic risk during the given period. It also helps investors identify the Innodata's relationships between the major components of the statement of changes in financial position and other commonly used cash-related accounts. When such correlations are discovered, they may help managers and analysts to enhance performance or determine appealing investment opportunities.

Investor sentiment, mood or attitude towards Innodata can have a significant impact on its stock price or the market as a whole. This sentiment can be positive or negative, and various factors, such as economic indicators, news events, or market trends, can influence it. When investor sentiment is positive, investors are more likely to buy stocks, increasing demand and increasing the stock price. Positive investor sentiment can be driven by good news about the company or the broader market, such as solid earnings reports or positive economic data.

Note that negative investor sentiment can cause investors to sell stocks, leading to a decrease in demand and a drop in the stock price. Negative sentiment can be driven by factors such as poor earnings reports, negative news about the company or industry, or broader economic concerns. It's important to note that investor sentiment is just one of many factors that can affect stock prices. Other factors, such as company performance, industry trends, and global economic conditions, can also play a significant role in determining the value of a stock.

Investor biases related to Innodata's public news can be used to forecast risks associated with an investment in Innodata. The trend in average sentiment can be used to explain how an investor holding Innodata can time the market purely based on public headlines and social activities around Innodata. Please note that most equities that are difficult to arbitrage are affected by market sentiment the most.

Innodata's market sentiment shows the aggregated news analyzed to detect positive and negative mentions from the text and comments. The data is normalized to provide daily scores for Innodata and other traded tickers. The bigger the bubble, the more accurate the estimated score. Higher bars for a given day show more participation in the average Innodata news discussions. The higher the estimate score, the more favorable the investor's outlook on Innodata.

Innodata's options can also be used to analyze investors' bias and current market sentiment in the context of behavioral finance. For example, Max pain usually refers to a trading concept that asserts that market manipulation can cause the market price of Innodata close to the expiration of its current option contract to expire worthlessly. According to most research, about 35% of options are not executed, with roughly 50% traded out before expiration. So, Max pain occurs when market makers reach a net favorable position across all options at a strike price where option holders stand to lose the most money. By contrast, option sellers may reap the most after selling more options than buying, causing them to expire worthlessly. Please continue to view the detailed analysis of Innodata's options.

We analyze Innodata's financials across various querterly and yearly statements, indicators and fundamental ratios. We help investors to determine the real value of Innodata using virtually all public information available. We use both quantitative as well as qualitative analysis to arrive at the intrinsic value of Innodata based on its fundamental data. In general, a quantitative approach, as applied to this company, focuses on analyzing financial statements comparatively, whereas a qaualitative method uses data that is important to a company's growth but cannot be measured and presented in a numerical way.

One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with Innodata stock to make a market-neutral strategy. Peer analysis of Innodata could also be used in its relative valuation, which is a method of valuing Innodata by comparing valuation metrics with similar companies.

When running Innodata's price analysis, check to measure Innodata's market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Innodata is operating at the current time. Most of Innodata's value examination focuses on studying past and present price action to predict the probability of Innodata's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Innodata's price. Additionally, you may evaluate how the addition of Innodata to your portfolios can decrease your overall portfolio volatility.