{"id":23989745,"url":"https://github.com/sarvsav/dissertation","last_synced_at":"2025-04-28T18:34:53.173Z","repository":{"id":186054467,"uuid":"581082168","full_name":"sarvsav/dissertation","owner":"sarvsav","description":"Bits dissertation on stock price prediction","archived":false,"fork":false,"pushed_at":"2023-04-03T09:54:51.000Z","size":26455,"stargazers_count":2,"open_issues_count":0,"forks_count":3,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-03-30T11:51:12.873Z","etag":null,"topics":["bits-pilani","dissertation","dissertation-project","machine-learning","numerical-analysis","stock-price-prediction","textual-analysis"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sarvsav.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-12-22T08:19:40.000Z","updated_at":"2025-03-15T19:02:54.000Z","dependencies_parsed_at":null,"dependency_job_id":"7801e966-4ba2-4639-9363-8e10fbf2cf07","html_url":"https://github.com/sarvsav/dissertation","commit_stats":null,"previous_names":["sarvsav/dissertation"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sarvsav%2Fdissertation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sarvsav%2Fdissertation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sarvsav%2Fdissertation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sarvsav%2Fdissertation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sarvsav","download_url":"https://codeload.github.com/sarvsav/dissertation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251366704,"owners_count":21578173,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bits-pilani","dissertation","dissertation-project","machine-learning","numerical-analysis","stock-price-prediction","textual-analysis"],"created_at":"2025-01-07T17:38:02.020Z","updated_at":"2025-04-28T18:34:53.155Z","avatar_url":"https://github.com/sarvsav.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# dissertation\nBits dissertation\n\nThe value of stocks always fluctuates dramatically over time, making stock market prediction\nan intriguing study subject. Investors do two different forms of study before buying a stock.\nThe fundamental analysis is one of the first approaches and very common. In order to\ndetermine whether to invest or not, investors consider factors such as the intrinsic worth of the\nstocks, the state of the market and economy, the political environment, etc. On the other side,\ntechnical analysis evaluates equities by looking at data produced by market activity, such as\nprevious prices and volumes. Typically of attempting to determine a security's fundamental\nworth, technical analysts instead utilize stock charts to spot patterns and trends that could\npredict how a stock will act in the future.\nMany methods for predicting stock movements have been developed throughout the years.\nInitially, stock trend predictions were made using traditional regression techniques. Non-linear\nmachine learning methods have also been applied since stock data may be characterized as\nnon-stationary time series data.\nWith a forget gate present, the LINEAR, POLY is similar to a long short-term memory (SVM),\nhowever it has fewer parameters than the SVM since it lacks an output gate. The vanishing\ngradient issue that arises when using a conventional scaler is addressed with LINEAR, POLY.\nThe time sequence is erratic and disordered. Most of the forecasting model that uncovers the\ncomplex connection between financial information about an industry and its stock price is\nbeneficial. The financial news in addition to the existing records concerning the firm is used to\nforecast future stock prices.\nSemantic and linguistic traits may be extracted using a variety of ways. The following are a\nfew of them: OpinionFinder, SentiWordNet, Linguistic Inquiry and Word Count (LIWC),\nGoogle Profile of Mood States (GPOMS), R sentiment analysis, and Python NLP package. In\nthis approach, the sentimental score is also calculated based on news headlines, in addition to\nthe statistical data for the model to produce more reliable results.\n\n\n![bits-pilani](https://www.bits-pilani.ac.in/Uploads/Campus/BITS_university_logo.gif)\n\n![image](https://user-images.githubusercontent.com/3764754/222978900-65a95405-4ff3-40d2-bf9d-60ee2fdd08dc.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsarvsav%2Fdissertation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsarvsav%2Fdissertation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsarvsav%2Fdissertation/lists"}